Detecting fever from video images and a baseline

ABSTRACT

Described herein are embodiments of systems and methods that utilize images of a user&#39;s face to detect fever and intoxication. One embodiment of a system to detect fever includes first and second inward-facing head-mounted cameras that are located less than 5 cm from a user&#39;s face, are sensitive to wavelengths below 1050 nanometer, and are configured to capture images of respective first and second regions on the user&#39;s face. The system also includes a computer that calculates, based on baseline images captured with the cameras while the user did not have a fever, a baseline pattern of hemoglobin concentrations at regions on the face. The computer also calculates, based on a current set of images captured with the cameras, a current pattern of hemoglobin concentrations at the regions, and detects whether the user has a fever based on a deviation of the current pattern from the baseline pattern.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/928,726, filed Oct. 31, 2019, U.S. Provisional Patent ApplicationNo. 62/945,141, filed Dec. 7, 2019, U.S. Provisional Patent ApplicationNo. 62/960,913, filed Jan. 14, 2020, U.S. Provisional Patent ApplicationNo. 63/006,827, filed Apr. 8, 2020, U.S. Provisional Patent ApplicationNo. 63/024,471, filed May 13, 2020, and U.S. Provisional PatentApplication No. 63/048,638, filed Jul. 6, 2020.

This application is a Continuation-In-Part of U.S. application Ser. No.16/689,959, filed Nov. 20, 2019, which claims priority to U.S.Provisional Patent Application No. 62/874,430, filed Jul. 15, 2019.

This application is also a Continuation-In-Part of U.S. application Ser.No. 16/854,883, filed Apr. 21, 2020, which is a Continuation-In-Part ofU.S. application Ser. No. 16/453,993, filed Jun. 26, 2019, now U.S. Pat.No. 10,667,697.

This application is also a Continuation-In-Part of U.S. application Ser.No. 16/831,413, filed Mar. 26, 2020, which is a Continuation-In-Part ofU.S. application Ser. No. 16/551,654, filed Aug. 26, 2019, now U.S. Pat.No. 10,638,938. U.S. Ser. No. 16/551,654 is a Continuation-In-Part ofU.S. application Ser. No. 16/453,993, filed Jun. 26, 2019. U.S. Ser. No.16/453,993 is a Continuation-In-Part of U.S. application Ser. No.16/375,841, filed Apr. 4, 2019. U.S. Ser. No. 16/375,841 is aContinuation-In-Part of U.S. application Ser. No. 16/156,493, now U.S.Pat. No. 10,524,667, filed Oct. 10, 2018. U.S. Ser. No. 16/156,493, is aContinuation-In-Part of U.S. application Ser. No. 15/635,178, filed Jun.27, 2017, now U.S. Pat. No. 10,136,856, which claims priority to U.S.Provisional Patent Application No. 62/354,833, filed Jun. 27, 2016, andU.S. Provisional Patent Application No. 62/372,063, filed Aug. 8, 2016.

U.S. Ser. No. 16/156,493 is also a Continuation-In-Part of U.S.application Ser. No. 15/231,276, filed Aug. 8, 2016, which claimspriority to U.S. Provisional Patent Application No. 62/202,808, filedAug. 8, 2015, and U.S. Provisional Patent Application No. 62/236,868,filed Oct. 3, 2015.

U.S. Ser. No. 16/156,493 is also a Continuation-In-Part of U.S.application Ser. No. 15/832,855, filed Dec. 6, 2017, now U.S. Pat. No.10,130,308, which claims priority to U.S. Provisional Patent ApplicationNo. 62/456,105, filed Feb. 7, 2017, and U.S. Provisional PatentApplication No. 62/480,496, filed Apr. 2, 2017, and U.S. ProvisionalPatent Application No. 62/566,572, filed Oct. 2, 2017. U.S. Ser. No.15/832,855 is a Continuation-In-Part of U.S. application Ser. No.15/182,592, filed Jun. 14, 2016, now U.S. Pat. No. 10,165,949, aContinuation-In-Part of U.S. application Ser. No. 15/231,276, filed Aug.8, 2016, a Continuation-In-Part of U.S. application Ser. No. 15/284,528,filed Oct. 3, 2016, now U.S. Pat. No. 10,113,913, a Continuation-In-Partof U.S. application Ser. No. 15/635,178, filed Jun. 27, 2017, now U.S.Pat. No. 10,136,856, and a Continuation-In-Part of U.S. application Ser.No. 15/722,434, filed Oct. 2, 2017.

U.S. Ser. No. 15/832,855 is a Continuation-In-Part of U.S. applicationSer. No. 15/182,566, filed Jun. 14, 2016, now U.S. Pat. No. 9,867,546,which claims priority to U.S. Provisional Patent Application No.62/175,319, filed Jun. 14, 2015, and U.S. Provisional Patent ApplicationNo. 62/202,808, filed Aug. 8, 2015.

U.S. Ser. No. 15/182,592 claims priority to U.S. Provisional PatentApplication No. 62/175,319, filed Jun. 14, 2015, and U.S. ProvisionalPatent Application No. 62/202,808, filed Aug. 8, 2015.

U.S. Ser. No. 15/284,528 claims priority to U.S. Provisional PatentApplication No. 62/236,868, filed Oct. 3, 2015, and U.S. ProvisionalPatent Application No. 62/354,833, filed Jun. 27, 2016, and U.S.Provisional Patent Application No. 62/372,063, filed Aug. 8, 2016.

U.S. Ser. No. 16/156,493 is also a Continuation-In-Part of U.S.application Ser. No. 15/833,115, filed Dec. 6, 2017, now U.S. Pat. No.10,130,261. U.S. Ser. No. 15/833,115 is a Continuation-In-Part of U.S.application Ser. No. 15/182,592, a Continuation-In-Part of U.S.application Ser. No. 15/231,276, filed Aug. 8, 2016, aContinuation-In-Part of U.S. application Ser. No. 15/284,528, aContinuation-In-Part of U.S. application Ser. No. 15/635,178, and aContinuation-In-Part of U.S. application Ser. No. 15/722,434, filed Oct.2, 2017.

U.S. Ser. No. 16/453,993 is also a Continuation-In-Part of U.S.application Ser. No. 16/147,695, filed Sep. 29, 2018. U.S. Ser. No.16/147,695 is a Continuation of U.S. application Ser. No. 15/182,592,filed Jun. 14, 2016, which claims priority to U.S. Provisional PatentApplication No. 62/175,319, filed Jun. 14, 2015, and U.S. ProvisionalPatent Application No. 62/202,808, filed Aug. 8, 2015.

This application is a Continuation-In-Part of U.S. Ser. No. 16/689,929,filed Nov. 20, 2019, that is a Continuation-In-Part of U.S. Ser. No.16/156,586, filed Oct. 10, 2018, that is a Continuation-In-Part of U.S.application Ser. No. 15/832,815, filed Dec. 6, 2017, which claimspriority to U.S. Provisional Patent Application No. 62/456,105, filedFeb. 7, 2017, and U.S. Provisional Patent Application No. 62/480,496,filed Apr. 2, 2017, and U.S. Provisional Patent Application No.62/566,572, filed Oct. 2, 2017. U.S. Ser. No. 16/156,586 is also aContinuation-In-Part of U.S. application Ser. No. 15/859,772 Jan. 2,2018, now U.S. Pat. No. 10,159,411.

ACKNOWLEDGMENTS

Gil Thieberger would like to thank his holy and beloved teacher, LamaDvora-hla, for her extraordinary teachings and manifestation of wisdom,love, compassion and morality, and for her endless efforts, support, andskills in guiding him and others on their paths to freedom and ultimatehappiness. Gil would also like to thank his beloved parents for raisinghim with love and care.

BACKGROUND

Fever is a common symptom of many medical conditions: infectiousdisease, such as COVID-19, dengue, Ebola, gastroenteritis, influenza,Lyme disease, malaria, as well as infections of the skin. It isimportant to track fever in order to be able to identify when a personmight be sick and should be isolated (when an infectious disease issuspected). Unfortunately, there is no easy and relatively inexpensiveway to continuously track fever. The most common way to track feverinvolves using a thermometer, which interrupts day-to-day activities.Using high-quality thermal cameras is significantly more expensive thanusing visible-light and/or near-infrared cameras, and thus is not aviable solution in many cases. As a result, there is a need for arelatively inexpensive and unobtrusive way to accurately detect whethera user has a fever, without interrupting the user's daily activities.

SUMMARY

Described herein are embodiments of systems and methods that utilizeimages of a user's face in order to detect temperature changes on auser's face for various purposes such as detecting fever, estimatingcore body temperature, detecting intoxication, and additionalapplications. The images may be captured using different hardwaresetups. In some embodiments, the images are captured using one or moreinward-facing head-mounted cameras (e.g., one or more cameras attachedto, or embedded in, smartglasses frames).

In one embodiment, the system is able to detect whether the user has afever, and/or estimate the user's core body temperature, optionallywithout using a thermal camera. In another embodiment, the system isable to detect whether the user has a fever, and/or the user's core bodytemperature, without receiving a temperature reading of the skin areaabove the temporal artery.

Some of the embodiments described herein have one or more of thefollowing advantages: there is no need to detect the region of skinabove the temporal artery, the system may operate well without measuringthe temperature of the region of skin above the temporal artery, and theimages captured by the camera sensitive to wavelengths below 1050nanometer may be indicative of extent of thermal interference from theenvironment.

Some aspects of this disclosure involve utilization of sensors that arephysically coupled to smartglasses in order to conveniently, andoptionally continuously, monitor users. Smartglasses are generallycomfortable to wear, lightweight, and can have extended battery life.Thus, they are well suited as an instrument for long-term monitoring ofpatient's physiological signals and activity, in order to determinewhether the user has a fever and/or whether the user is intoxicated.

One aspect of this disclosure involves a system configured to detectfever. In one embodiment, the system includes first and secondinward-facing head-mounted cameras (denoted Cam_(1&2)). Cam_(1&2) arelocated less than 5 cm from a user's face, are sensitive to wavelengthsbelow 1050 nanometer, and are configured to capture images of respectivefirst and second regions on the user's face. Optionally, the middles ofthe first and second regions are at least 4 cm apart. In one example,the first region is located above the user's eyes, and the second regionis located below the user's eyes. In another example, the middle of thefirst region is located less than 4 cm from the vertical symmetric axisof the user's face, and the middle of the second region is located morethan 4 cm from the vertical symmetric axis.

The system also includes a computer, which is configured to perform thefollowing: calculate, based on baseline images captured with Cam_(1&2)while the user did not have a fever, a baseline pattern comprisingvalues indicative of first and second baseline hemoglobin concentrationsat the first and second regions, respectively; calculate, based on acurrent set of images captured with Cam_(1&2), a current patterncomprising values indicative of first and second current hemoglobinconcentrations at the first and second regions, respectively; and detectwhether the user has a fever based on a deviation of the current patternfrom the baseline pattern. Optionally, the computer calculates thevalues indicative of the baseline and current hemoglobin concentrationsbased on detecting imaging photoplethysmogram signals in the baselineand the current set of images.

In one embodiment, the computer also calculates, based on additionalbaseline images captured with Cam_(1&2) while the user had a fever, afever-baseline pattern comprising values indicative of first and secondfever hemoglobin concentrations at the first and second regions,respectively. In this embodiment, the computer bases the detection ofwhether the user has the fever also on a deviation of the currentpattern from the fever-baseline pattern.

In one embodiment, the baseline images and the current set of imagescomprise a first channel corresponding to wavelengths that are mostlybelow 580 nanometers and a second channel corresponding to wavelengthsmostly above 580 nanometers; the baseline pattern comprises: (i) firstvalues, derived based on the first channel in the baseline images, whichare indicative of the first and second baseline hemoglobinconcentrations at the first and second regions, respectively, and (ii)second values, derived based on the second channel in the baselineimages, which are indicative of third and fourth baseline hemoglobinconcentrations at the first and second regions, respectively. Thecurrent pattern comprises: (i) third values, derived based on the firstchannel in the current set of images, which are indicative of the firstand second current hemoglobin concentrations at the first and secondregions, respectively, and (ii) fourth values, derived based on thesecond channel in the current set of images, which are indicative ofthird and fourth current hemoglobin concentrations at the first andsecond regions, respectively. Optionally, having separate values fordifferent wavelengths enables to account for interference from theenvironment when detecting whether the user has the fever becausetemperature interference from the environment is expected to affect thethird values more than the fourth values. Optionally, the computercalculates a confidence in a detection of the fever based on thedeviation of the current pattern from the baseline pattern, such thatthe confidence decreases as the difference between the third values andthe fourth values increases.

In some embodiments, the computer may detect additional physiologicalsignals or conditions based on the deviation of the current pattern fromthe baseline pattern. In one example, the computer detects blushingbased on the deviation of the current pattern from the baseline pattern,and presents an alert to the user about the blushing. In anotherembodiment, the computer utilizes one or more calibration measurementsof the user's core body temperature, taken by a different device, priorto a certain time, to calculate the user's core body temperature basedon a certain set of images that were taken by Cam_(1&2) after thecertain time.

Another aspect of this disclosure includes a method for detecting feverwhich includes the following steps: In Step 1, receiving, from first andsecond inward-facing head-mounted cameras (Cam_(1&2)) sensitive towavelengths below 1050 nanometer, images of respective first and secondregions on a user's face. Optionally, the middles of the first andsecond regions are at least 4 cm apart. In Step 2, calculating, based onbaseline images captured with Cam_(1&2) while the user did not have afever, a baseline pattern comprising values indicative of first andsecond baseline hemoglobin concentrations at the first and secondregions, respectively. In Step 3, calculating, based on a current set ofimages captured with Cam_(1&2), a current pattern comprising valuesindicative of first and second current hemoglobin concentrations at thefirst and second regions, respectively. And in Step 4, detecting whetherthe user has a fever based on a deviation of the current pattern fromthe baseline pattern.

In one embodiment, the method for detecting fever optionally includesthe following steps: calculating, based on additional baseline imagescaptured with Cam_(1&2) while the user had a fever, a fever-baselinepattern comprising values indicative of first and second feverhemoglobin concentrations at the first and second regions, respectively;and basing the detecting of whether the user has the fever also on adeviation of the current pattern from the fever-baseline pattern.

Yet another aspect of this disclosure involves a system configured todetect alcohol intoxication. In one embodiment, the system includesfirst and second inward-facing head-mounted cameras (denoted Cam_(1&2)).Cam_(1&2) are located less than 5 cm from a user's face, are sensitiveto wavelengths below 1050 nanometer, and are configured to captureimages of respective first and second regions on the user's face.Optionally, the middles of the first and second regions are at least 4cm apart. In one example, the first region is located above the user'seyes, and the second region is located below the user's eyes. In anotherexample, the middle of the first region is located less than 4 cm fromthe vertical symmetric axis of the user's face, and the middle of thesecond region is located more than 4 cm from the vertical symmetricaxis. The system also includes a computer, which is configured toperform the following: calculate, based on baseline images captured withCam_(1&2) while the user did not have a fever, a baseline patterncomprising values indicative of first and second baseline hemoglobinconcentrations at the first and second regions, respectively; calculate,based on a current set of images captured with Cam_(1&2), a currentpattern comprising values indicative of first and second currenthemoglobin concentrations at the first and second regions, respectively;and detect whether the user is intoxicated based on a deviation of thecurrent pattern from the baseline pattern. Optionally, the computercalculates the values indicative of the baseline and current hemoglobinconcentrations based on detecting facial flushing patterns in thebaseline and current images.

In one embodiment, the computer also calculates, based on additionalbaseline images captured with Cam_(1&2) while the user was intoxicated,an intoxication-baseline pattern comprising values indicative of firstand second intoxication hemoglobin concentrations at the first andsecond regions, respectively. In this embodiment, the computer bases thedetection of whether the user is intoxicated also based on a deviationof the current pattern from the intoxication-baseline pattern.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments are herein described by way of example only, withreference to the following drawings:

FIG. 1a illustrates an example of a hemoglobin concentration pattern ofa sober person;

FIG. 1b illustrates an example of the hemoglobin concentration patternof the same person when intoxicated;

FIG. 1c is a schematic illustration of components of a system configuredto detect fever and/or intoxication;

FIG. 2a illustrates an embodiment of a system that utilizes multiple PPGsignals, measured using different types of sensors, to detect aphysiological response;

FIG. 2b illustrates smartglasses that include a camera and severalcontact PPG devices;

FIG. 2c illustrates smartglasses that include at least first, second,and third inward-facing cameras;

FIG. 2d is a schematic illustration of some of the various fiducialpoints for PPG signals often used in the art;

FIG. 3a and FIG. 3b illustrate various inward-facing head-mountedcameras coupled to an eyeglasses frame;

FIG. 4 illustrates inward-facing head-mounted cameras coupled to anaugmented reality device;

FIG. 5 illustrates head-mounted cameras coupled to a virtual realitydevice;

FIG. 6 illustrates a side view of head-mounted cameras coupled to anaugmented reality device;

FIG. 7 illustrates a side view of head-mounted cameras coupled to asunglasses frame;

FIG. 8, FIG. 9, FIG. 10 and FIG. 11 illustrate head-mounted systems(HMSs) configured to measure various ROIs relevant to some of theembodiments describes herein;

FIG. 12, FIG. 13, FIG. 14 and FIG. 15 illustrate various embodiments ofsystems that include inward-facing head-mounted cameras havingmulti-pixel sensors (FPA sensors);

FIG. 16a , FIG. 16b , and FIG. 16c illustrate embodiments of two rightand left clip-on devices that are configured to attached/detached froman eyeglasses frame;

FIG. 17a and FIG. 17b illustrate an embodiment of a clip-on device thatincludes inward-facing head-mounted cameras pointed at the lower part ofthe face and the forehead;

FIG. 18a and FIG. 18b illustrate embodiments of right and left clip-ondevices that are configured to be attached behind an eyeglasses frame;

FIG. 19a and FIG. 19b illustrate an embodiment of a single-unit clip-ondevice that is configured to be attached behind an eyeglasses frame;

FIG. 20 illustrates embodiments of right and left clip-on devices, whichare configured to be attached/detached from an eyeglasses frame, andhave protruding arms to hold inward-facing head-mounted cameras;

FIG. 21 illustrates a scenario in which an alert regarding a possiblestroke is issued;

FIG. 22a is a schematic illustration of an inward-facing head-mountedcamera embedded in an eyeglasses frame, which utilizes the Scheimpflugprinciple;

FIG. 22b is a schematic illustration of a camera that is able to changethe relative tilt between its lens and sensor planes according to theScheimpflug principle;

FIG. 23 illustrates an embodiment of a system that collects thermalmeasurements related to respiration, in which four inward-facinghead-mounted thermal cameras (CAMs) are coupled to a football helmet;

FIG. 24 illustrates a situation in which an alert is issued to a userwhen it is detected that the ratio the duration of exhaling and inhalingis too low;

FIG. 25 illustrates an embodiment of a system that collects thermalmeasurements related to respiration, in which four CAMs are coupled tothe bottom of an eyeglasses frame;

FIG. 26a , FIG. 26b , FIG. 27a , FIG. 27b and FIG. 27c illustrate howembodiments described herein may help train an elderly user to exhaleduring effort;

FIG. 28a and FIG. 28b illustrate a fitness app running on smartphone,which instructs the user to exhale while bending down, and to inhalewhile straightening up;

FIG. 29 illustrates a fitness app running on smartphone, which instructsthe user to stay in a triangle pose for 8 breath cycles;

FIG. 30 illustrates notifying a user about mouth breathing andsuggesting to breathe through the nose;

FIG. 31 illustrates an exemplary UI that shows statistics about thedominant nostril and mouth breathing during the day;

FIG. 32 illustrates a virtual robot that the user sees via augmentedreality (AR), which urges the user to increase the ratio between theduration of the user's exhales and inhales;

FIG. 33 illustrates an asthmatic patient who receives an alert that hisbreathing rate increased to an extent that often precedes an asthmaattack;

FIG. 34a is a schematic illustration of a left dominant nostril;

FIG. 34b is a schematic illustration of a right dominant nostril;

FIG. 34c is a schematic illustration of balanced breathing;

FIG. 35 is a schematic illustration of an embodiment of a system thatidentifies the dominant nostril;

FIG. 36a illustrates an embodiment of a system that suggests activitiesaccording to the dominant nostril;

FIG. 36b illustrates an embodiment of a system for calculating arespiratory parameter;

FIG. 37a illustrates an embodiment of a system for estimating an aerobicactivity parameter;

FIG. 37b illustrates an embodiment of an athletic coaching system;

FIG. 37c illustrates a cycler who receives breathing cues via an earbud;

FIG. 37d illustrates a user receiving coaching instructions whilehitting a driver in golf;

FIG. 38 illustrates an embodiment of a system configured to provideneurofeedback and/or breathing biofeedback;

FIG. 39, FIG. 40 and FIG. 41 illustrate an embodiment of eyeglasses withhead-mounted thermal cameras, which are able to differentiate betweendifferent states of the user based on thermal patterns of the forehead;

FIG. 42 illustrates an embodiment of a clip-on device configured to beattached and detached from a frame of eyeglasses multiple times;

FIG. 43 illustrates a scenario in which a user has neurofeedback sessionduring a day-to-day activity;

FIG. 44a and FIG. 44b illustrate the making of different detections ofemotional response based on thermal measurements compared to theemotional response that is visible in a facial expression;

FIG. 45 illustrates an embodiment of a smartphone app that provides auser with feedback about how he/she looks to others;

FIG. 46 illustrates one embodiment of a tablet app that provides theuser a feedback about how he/she felt during a certain period;

FIG. 47 illustrates an embodiment of the system configured to detect aphysiological response based on facial skin color changes (FSCC);

FIG. 48a and FIG. 48b illustrate heating of a ROI for different reasons:sinusitis (which is detected), and acne (which is not detected assinusitis);

FIG. 49a and FIG. 49b illustrate an embodiment of a system that providesindications when the user touches his/her face;

FIG. 50a illustrates a first case where a user's hair does not occludethe forehead;

FIG. 50b illustrates a second case where a user's hair occludes theforehead and the system requests the user to move the hair in order toenable correct measurements of the forehead;

FIG. 51a illustrates an embodiment of a system that detects aphysiological response based on measurements taken by an inward-facinghead-mounted thermal camera and an outward-facing head-mounted thermalcamera;

FIG. 51b illustrates a scenario in which a user receives an indicationon a GUI that the user is not monitored in direct sunlight;

FIG. 52 illustrates a case in which a user receives an indication thatshe is not being monitored in a windy environment;

FIG. 53 illustrates an elderly person whose facial temperature increasesas a result of bending over;

FIG. 54 illustrates the effect of consuming alcohol on values of thermalmeasurements;

FIG. 55 illustrates an increase in the thermal measurements due tosmoking;

FIG. 56 illustrates a decrease in the thermal measurements due to takingmedication; and

FIG. 57a and FIG. 57b are schematic illustrations of possibleembodiments for computers.

DETAILED DESCRIPTION

Herein the terms “photoplethysmogram signal”, “photoplethysmographicsignal”, “photoplethysmography signal”, and other similar variations areinterchangeable and refer to the same type of signal. Aphotoplethysmogram signal may be referred to as a “PPG signal”, or an“iPPG signal” when specifically referring to a PPG signal obtained froma camera. The terms “photoplethysmography device”,“photoplethysmographic device”, “photoplethysmogram device”, and othersimilar variations are also interchangeable and refer to the same typeof device that measures a signal from which it is possible to extractthe photoplethysmogram signal. The photoplethysmography device may bereferred to as “PPG device”.

Sentences in the form of “a sensor configured to measure a signalindicative of a photoplethysmogram signal” refer to at least one of: (i)a contact PPG device, such as a pulse oximeter that illuminates the skinand measures changes in light absorption, where the changes in lightabsorption are indicative of the PPG signal, and (ii) a non-contactcamera that captures images of the skin, where a computer extracts thePPG signal from the images using an imaging photoplethysmography (iPPG)technique. Other names known in the art for iPPG include: remotephotoplethysmography (rPPG), remote photoplethysmographic imaging,remote imaging photoplethysmography, remote-PPG, and multi-sitephotoplethysmography (MPPG).

A PPG signal is often obtained by using a pulse oximeter, whichilluminates the skin and measures changes in light absorption. Anotherpossibility for obtaining the PPG signal is using an imagingphotoplethysmography (iPPG) device. As opposed to contact PPG devices,iPPG does not require contact with the skin and is obtained by anon-contact sensor, such as a video camera.

A time series of values measured by a PPG device, which is indicative ofblood flow changes due to pulse waves, is typically referred to as awaveform (or PPG waveform to indicate it is obtained with a PPG device).It is well known that PPG waveforms show significant gender-relateddifferences, age-related differences, and health-related differences. Asa result, the PPG waveforms of different people often display differentcharacteristics (e.g., slightly different shapes and/or amplitudes). Inaddition, the PPG waveform depends on the site at which it is measured,skin temperature, skin tone, and other parameters.

The analysis of PPG signals usually includes the following steps:filtration of a PPG signal (such as applying bandpass filtering and/orheuristic filtering), extraction of feature values from fiducial pointsin the PPG signal (and in some cases may also include extraction offeature values from non-fiducial points in the PPG signal), and analysisof the feature values.

One type of features that is often used when performing calculationsinvolving PPG signals involves fiducial points related to the waveformsof the PPG signal and/or to functions thereof (such as variousderivatives of the PPG signal). There are many known techniques toidentify the fiducial points in the PPG signal, and to extract thefeature values. The following are some non-limiting examples of how toidentify fiducial points.

FIG. 2d is a schematic illustration of some of the various fiducialpoints often used in the art (and described below). These examples offiducial points include fiducial points of the PPG signal, fiducialpoints in the first derivative of the PPG signal (velocityphotoplethysmogram, VPG), and fiducial points in the second derivativeof the PPG signal (acceleration photoplethysmogram, APG).

Fiducial points in the PPG signal may include: the systolic notch 920,which is the minimum at the PPG signal onset; the systolic peak 921,which is the maximum of the PPG signal; the dicrotic notch 922, whichcoincident with e 934 (see below at the second derivative of the PPGsignal); and the diastolic peak 923, which is the first local maximum ofthe PPG signal after the dicrotic notch and before 0.8 of the durationof the cardiac cycle, or if there is no such local maximum, then thefirst local maximum of the second derivative after e and before 0.8 ofthe duration of the cardiac cycle.

Fiducial points in the first derivative of the PPG signal (velocityphotoplethysmogram, VPG) may include: the maximum slope peak in systolicof VPG 925; the local minima slope in systolic of VPG 926; the globalminima slope in systolic of VPG 927; and the maximum slope peak indiastolic of VPG 928.

Fiducial points in the second derivative of the PPG signal (accelerationphotoplethysmogram, APG) may include: a 930, which is the maximum of APGprior to the maximum of VPG; b 931, which is the first local minimum ofAPG following a; c 932, which is the greatest maximum of APG between band e, or if no maxima then the first of (i) the first maximum of VPGafter e, and (ii) the first minimum of APG after e; d 933, which is thelowest minimum of APG after c and before e, or if no minima thencoincident with c; e 934, which is the second maximum of APG aftermaximum of VPG and before 0.6 of the duration of the cardiac cycle,unless the c wave is an inflection point, in which case take the firstmaximum; and f 935, which is the first local minimum of APG after e andbefore 0.8 of the duration of the cardiac cycle.

Fiducial points in the third derivative of the PPG signal (PPG′″) mayinclude: the first local maximum of PPG′″ after b; and the last localminimum of PPG′″ before d, unless c=d, in which case take the firstlocal minimum of PPG′″ after d, and if there is a local maximum of thePPG signal between this point and the dicrotic notch then use itinstead.

Feature values of the PPG signal may also be extracted fromrelationships in the PPG signal and/or its derivatives. The followingare some non-limiting examples such possible feature values: pulsewidth, peak to peak time, ratio of areas before and after dicrotic notchin a complete cycle, baseline wander (BW), which is the mean of theamplitudes of a beat's peak and trough; amplitude modulation (AM), whichis the difference between the amplitudes of each beat's peak and trough;and frequency modulation (FM), which is the time interval betweenconsecutive peaks.

Examples of additional features that can be extracted from the PPGsignal, together with schematic illustrations of the feature locationson the PPG signal, can be found in the following three publications: (i)Peltokangas, Mikko, et al. “Parameters extracted from arterial pulsewaves as markers of atherosclerotic changes: performance andrepeatability.” IEEE journal of biomedical and health informatics 22.3(2017): 750-757; (ii) Ahn, Jae Mok. “New aging index using signalfeatures of both photoplethysmograms and acceleration plethysmograms.”Healthcare informatics research 23.1 (2017): 53-59; (iii) Charlton,Peter H., et al. “Assessing mental stress from the photoplethysmogram: anumerical study.” Physiological measurement 39.5 (2018): 054001, and(iv) Peralta, Elena, et al. “Optimal fiducial points for pulse ratevariability analysis from forehead and finger photoplethysmographicsignals.” Physiological measurement 40.2 (2019): 025007.

Although the above mentioned references describe manual featureselection, the features may be selected using any appropriate featureengineering technique, including using automated feature engineeringtools that help data scientists to reduce data exploration time, andenable non-experts, who may not be familiar with data science and/or PPGcharacteristics, to quickly extract value from their data with littleeffort.

Unless there is a specific reference to a specific derivative of the PPGsignal, phrases of the form of “based on the PPG signal” refer to thePPG signal and any derivative thereof, including the first derivative ofthe PPG signal, the second derivative of the PPG signal, and the thirdderivative of the PPG signal. For example, a sentence in the form of “acomputer configured to detect a physiological signal based on the PPGsignal” is to be interpreted as “a computer configured to detect aphysiological signal based on at least one of: the PPG signal, a firstderivative of the PPG signal, a second derivative of the PPG signal, athe third derivative of the PPG signal, and/or any other derivative ofthe PPG signal”.

Algorithms for filtration of the PPG signal, extraction of featurevalues from fiducial points in the PPG signal, and analysis of thefeature values extracted from the PPG signal are well known in the art,and can be found for example in the following references: (i) Allen,John. “Photoplethysmography and its application in clinicalphysiological measurement.” Physiological measurement 28.3 (2007): R1,and also in the thousands of references citing this reference; (ii)Elgendi, Mohamed. “On the analysis of fingertip photoplethysmogramsignals.” Current cardiology reviews 8.1 (2012): 14-25, and also in thehundreds of references citing this reference; (iii) Holton, Benjamin D.,et al. “Signal recovery in imaging photoplethysmography.” Physiologicalmeasurement 34.11 (2013): 1499, and also in the dozens of referencesciting this reference, (iv) Sun, Yu, and Nitish Thakor.“Photoplethysmography revisited: from contact to noncontact, from pointto imaging” IEEE Transactions on Biomedical Engineering 63.3 (2015):463-477, and also in the dozens of references citing this reference, (v)Kumar, Mayank, Ashok Veeraraghavan, and Ashutosh Sabharwal.“DistancePPG: Robust non-contact vital signs monitoring using a camera.”Biomedical optics express 6.5 (2015): 1565-1588, and also in the dozensof references citing this reference, (vi) Wang, Wenjin, et al.“Algorithmic principles of remote PPG.” IEEE Transactions on BiomedicalEngineering 64.7 (2016): 1479-1491, and also in the dozens of referencesciting this reference, and (vii) Rouast, Philipp V., et al. “Remoteheart rate measurement using low-cost RGB face video: a technicalliterature review.” Frontiers of Computer Science 12.5 (2018): 858-872,and also in the dozens of references citing this reference.

Various embodiments described herein involve calculations based onmachine learning approaches. Herein, the terms “machine learningapproach” and/or “machine learning-based approaches” refer to learningfrom examples using one or more approaches. Examples of machine learningapproaches include: decision tree learning, association rule learning,regression models, nearest neighbors classifiers, artificial neuralnetworks, deep learning, inductive logic programming, support vectormachines, clustering, Bayesian networks, reinforcement learning,representation learning, similarity and metric learning, sparsedictionary learning, genetic algorithms, rule-based machine learning,and/or learning classifier systems.

Herein, a “machine learning-based model” is a model trained using one ormore machine learning approaches. For brevity's sake, at times, a“machine learning-based model” may simply be called a “model”. Referringto a model as being “machine learning-based” is intended to indicatethat the model is trained using one or more machine learning approaches(otherwise, “model” may also refer to a model generated by methods otherthan machine learning).

Herein, “feature values” (also known as feature vector, feature data,and numerical features) may be considered input to a computer thatutilizes a model to perform the calculation of a value, such as a valueindicative of one or more vital signs of a user. It is to be noted thatthe terms “feature” and “feature value” may be used interchangeably whenthe context of their use is clear. However, a “feature” typically refersto a certain type of value, and represents a property, while “featurevalue” is the value of the property with a certain instance (i.e., thevalue of the feature in a certain sample).

It is to be noted that when it is stated that feature values aregenerated based on data comprising multiple sources, it means that foreach source, there is at least one feature value that is generated basedon that source (and possibly other data). For example, stating thatfeature values are generated from an image capturing first and secondregions (IM_(ROI1) and IM_(ROI2), respectively) means that the featurevalues include at least a first feature value generated based onIM_(ROI1) and a second feature value generated based on IM_(ROI2).

In addition to feature values generated based on measurements taken bysensors mentioned in a specific embodiment, at least some feature valuesutilized by a computer of the specific embodiment may be generated basedon additional sources of data that were not specifically mentioned inthe specific embodiment. Some examples of such additional sources ofdata include: (i) contextual information such as the time of day (e.g.,to account for effects of the circadian rhythm), day of month (e.g., toaccount for effects of the lunar rhythm), day in the year (e.g., toaccount for seasonal effects), and/or stage in a menstrual cycle; (ii)information about the user being measured such as sex, age, weight,height, body build, genetics, medical records, and/or intake ofsubstances; (iii) measurements of the environment, such as temperature,humidity level, noise level, elevation, air quality, a wind speed,precipitation, and infrared radiation; and/or (iv) values ofphysiological signals of the user obtained by sensors that are notmentioned in the specific embodiment, such as an electrocardiogram (ECG)sensor, an electroencephalography (EEG) sensor, a galvanic skin response(GSR) sensor, a movement sensor, an acoustic sensor, and/or atemperature sensor.

A machine learning-based model of a specific embodiment may be trained,in some embodiments, based on data collected in day-to-day, real worldscenarios. As such, the data may be collected at different times of theday, while users perform various activities, and in variousenvironmental conditions. Utilizing such diverse training data mayenable a trained model to be more resilient to the various effects thatdifferent conditions can have on the measurements, and consequently, beable to achieve better detection of a required parameter in real worldthy-to-day scenarios.

The machine learning-based model may be personalized for a specificuser. For example, after receiving a verified diagnosis of an extent ofa physiological condition (such as blood pressure level, extent of acardiovascular disease, extent of a pulmonary disease, extent of amigraine attack, etc.), the computed can use the verified diagnosis aslabels and generate from a physiological measurement (such as the PPGsignal, the temperature signal, the movement signal, and/or the audiosignal) feature values to train a personalized machine learning-basedmodel for the user. Then the computer can utilize the personalizedmachine learning-based model for future calculations of the extent ofthe physiological condition based on feature values.

Sentences in the form of “inward-facing head-mounted camera” refer to acamera configured to be worn on a user's head and to remain pointed atits ROI, which is on the user's face, also when the user's head makesangular and lateral movements (such as movements with an angularvelocity above 0.1 rad/sec, above 0.5 rad/sec, and/or above 1 rad/sec).A head-mounted camera (which may be inward-facing and/or outward-facing)may be physically coupled to a frame worn on the user's head, may bephysically coupled to eyeglasses using a clip-on mechanism (configuredto be attached to and detached from the eyeglasses), may be physicallycoupled to a hat or a helmet, or may be mounted to the user's head usingany other known device that keeps the camera in a fixed positionrelative to the user's head also when the head moves. Sentences in theform of “sensor physically coupled to the frame” mean that the sensormoves with the frame, such as when the sensor is fixed to (or integratedinto) the frame, and/or when the sensor is fixed to (or integrated into)an element that is physically coupled to the frame, and/or when thesensor is connected to the frame with a clip-on mechanism.

Sentences in the form of “a frame configured to be worn on a user'shead” or “a frame worn on a user's head” refer to a mechanical structurethat loads more than 50% of its weight on the user's head. For example,an eyeglasses frame may include two temples connected to two rimsconnected by a bridge; the frame in Oculus Rift™ includes the foamplaced on the user's face and the straps; and the frame in Google Glass™is similar to an eyeglasses frame. Additionally or alternatively, theframe may connect to, be affixed within, and/or be integrated with, ahelmet (e.g., a safety helmet, a motorcycle helmet, a combat helmet, asports helmet, a bicycle helmet, etc.), goggles, and/or abrainwave-measuring headset.

Sentences in the form of “a frame configured to be worn on a user's headin a consistent manner” refer to a frame that is located in the sameposition relative to the head when worn repeatedly, and thus sensorsattached to that frame are most likely to be positioned each time at thesame location relative to the head. For example, eyeglasses frames,goggles, and helmets are all included under the definition of a framethat is worn in a consistent manner. However, a flexible headband, oradhesive sensors that are placed manually one by one, are not worn in aconsistent manner, because these sensors are most likely to bepositioned each time in a different location relative to the head.

The term “smartglasses” refers to any type of a device that remindseyeglasses, and includes a frame configured to be worn on a user's headin a consistent manner, and includes electronics to operate one or moresensors. The frame may be an integral part of the smartglasses, and/oran element that is connected to the smartglasses. Examples ofsmartglasses include: any type of eyeglasses with electronics (whetherprescription or plano), sunglasses with electronics, safety goggles withelectronics, sports goggle with electronics, augmented reality devices,virtual reality devices, and mixed reality devices. In addition, theterm “eyeglasses frame” refers to one or more of the following devices,whether with or without electronics: smartglasses, prescriptioneyeglasses, plano eyeglasses, prescription sunglasses, plano sunglasses,safety goggles, sports goggle, an augmented reality device, virtualreality devices, and a mixed reality device.

The term “smart-helmet” refers to a helmet that includes a frameconfigured to be worn on a user's head in a consistent manner, andincludes electronics to operate one or more sensors. The frame may be anintegral part of the smart-helmet, and/or an element that is connectedto the smart-helmet. Examples of smart-helmets include: a safety helmetwith electronics, a motorcycle helmet with electronics, a combat helmetwith electronics, a sports helmet with electronics, and a bicycle helmetwith electronics.

Examples of electronics that may be included in smartglasses and/or asmart-helmet include one or more of the following electronic components:a computer, a microcontroller, a processor, a memory, and acommunication interface. The electronics of the smartglasses and/orsmart-helmets may be integrated in various ways. For example, theelectronics may be integrated into the package of one of the sensors,such as a camera housing that is physically coupled to a helmet, wherethe housing includes the imaging sensor and its processor, memory, powersupply and wireless communication unit. In another example, theelectronics may be integrated into the frame, such as a microcontroller,power supply and wireless communication unit that are integrated into aneyeglasses frame, and configured to operate a PPG device and amicrophone that are physically coupled to the frame.

The term “Visible-light camera” refers to a non-contact device designedto detect at least some of the visible spectrum, such as a video camerawith optical lenses and CMOS or CCD sensor. The term “thermal camera”refers to a non-contact device that measures electromagnetic radiationhaving wavelengths longer than 2500 nanometer (nm) and does not touchits region of interest (ROI). A thermal camera may include one sensingelement (pixel), or multiple sensing elements that are also referred toherein as “sensing pixels”, “pixels”, and/or focal-plane array (FPA). Athermal camera may be based on an uncooled thermal sensor, such as athermopile sensor, a microbolometer sensor (where microbolometer refersto any type of a bolometer sensor and its equivalents), a pyroelectricsensor, or a ferroelectric sensor.

A reference to a “camera” herein may relate to various types of devices.In one example, a camera may be a visible-light camera. In anotherexample, a camera may capture light in the ultra-violet range. Inanother example, a camera may capture near infrared radiation (e.g.,wavelengths between 750 and 2000 nm). And in still another example, acamera may be a thermal camera.

When a camera is inward-facing and head-mounted, challenges faced bysystems known in the art that are used to acquire images, which includenon-head-mounted cameras, may be simplified and even eliminated withsome of the embodiments described herein. Some of these challenges mayinvolve dealing with complications caused by movements of the user,image registration, region of interest (ROI) alignment, tracking basedon hot spots or markers, and motion compensation.

The term “temperature sensor” refers to a device that measurestemperature and/or temperature change. The temperature sensor may be acontact thermometer (such as a thermistor, a thermocouple), and/or anon-contact thermal cameras (such as a thermopile sensor, amicrobolometer sensor, a pyroelectric sensor, or a ferroelectricsensor). Some examples of temperature sensors useful to measure skintemperature include: thermistors, thermocouples, thermoelectic effect,thermopiles, microbolometers, and pyroelectric sensors. Some examples oftemperature sensors useful to measure environment temperature include:thermistors, resistance temperature detectors, thermocouples;thermopiles, and semiconductor-based sensors.

The term “movement sensor” refers to a sensor comprising one or more ofthe following components: a 3-axis gyroscope, a 3-axis accelerometer,and a 3-axis magnetometer. The movement sensor may also include a sensorthat measures barometric pressure.

The term “acoustic sensor” refers to a device that converts sound wavesinto an electrical signal. An acoustic sensor can be a microphone, suchas a dynamic microphone that works via electromagnetic induction, apiezoelectric microphone that uses the phenomenon of piezoelectricity, afiber-optic microphone that converts acoustic waves into electricalsignals by sensing changes in light intensity, aMicro-Electrical-Mechanical System (MEMS) microphone (such as siliconMEMS and piezoelectric MEMS), and/or other sensors that measure soundwaves, such as described in the following examples: (i) Han, Jae Hyun,et al. “Basilar membrane-inspired self-powered acoustic sensor enabledby highly sensitive multi tunable frequency band.” Nano Energy 53(2018): 198-205, describes a self-powered flexible piezoelectricacoustic sensor having high sensitivity, (ii) Rao, Jihong, et al.“Recent Progress in Self-Powered Skin Sensors.” Sensors 19.12 (2019):2763. describes various self-powered acoustic skin sensors, such as anintegrated triboelectric nanogenerator (TENG) with a polymer tube thatcan pick up and recover human throat voice even in an extremely noisy orwindy environment, and (iii) Scanlon, Michael V. Acoustic sensor forvoice with embedded physiology. Army Research Lab Adelphi M D, 1999,describes a gel-coupled acoustic sensor able to collect informationrelated to the function of the heart, lungs, and changes in voicepatterns.

Herein, the term “blood pressure” is indicative of one or more of thefollowing: the systolic blood pressure of the user, the diastolic bloodpressure of the user, and the mean arterial pressure (MAP) of the user.It is specifically noted that the term “blood pressure” is not limitedto the systolic and diastolic blood pressure pair.

The terms “substance intake” or “intake of substances” refer to any typeof food, beverage, medications, drugs, smoking/inhaling, and anycombination thereof.

Blood flow in the face can cause certain facial coloration due toconcentration of hemoglobin in various vessels such as arterioles,capillaries, and venules. In some embodiments described herein,coloration at a certain facial region, and/or changes thereto (possiblydue to varying volume of blood in the certain region at different stagesof cardiac pulses), can represent a hemoglobin concentration pattern atthe certain region. This pattern can change because of various factorsthat can affect blood flow and/or vascular dilation, such as theexternal temperature, core body temperature, the emotional state,consumption of vascular dilating substances, and more. Embodimentsdescribed herein utilize analysis of images of the user's face, in whicha hemoglobin concentration pattern can be detected, in order to detectvarious phenomena that may influence facial temperature, such as havinga fever, being intoxicated, and/or in order to estimate physiologicalparameters such as the core body temperature.

In some embodiments, a hemoglobin concentration pattern calculated fromimages refers to a color mapping of various portions of the areacaptured in the images (e.g., the mapping provides the colors ofdifferent pixels in the images). In one example, the color mappingprovides values that are average intensities of one or more colors ofthe pixels over a period of time during which the images were taken(e.g., values from one or more channels in the images). In anotherexample, the color mapping provides values that are average intensitiesof one or more colors of the pixels over a period of time during whichthe images were taken (e.g., values of the maximum of one or morechannels in the images). In yet another example, a hemoglobinconcentration pattern may be a function of one or more colors (channels)of the pixels over a period of time during which the images were taken.

In other embodiments, a hemoglobin concentration pattern may refer totime series data, such as a sequence of images representing aprogression of a pulse wave in the area. Different physiologicalconditions, such as different skin or core body temperatures oremotional responses, may produce different sequences of representativeimages, which depend on the structure of the facial blood vessels of theuser and their dilation.

In still other embodiments, a hemoglobin concentration pattern may referto a contour map, representing the extent to which pixels at a certainwavelength (e.g., corresponding to the color red) have at least acertain value. Since the extent of hemoglobin concentration iscorrelated with an increase in intensity of certain colors (e.g., red),a hemoglobin concentration pattern for more dilated blood vessels willhave different contour map than the contour map observed in a hemoglobinconcentration pattern for that blood vessels when it is more contracted.

A hemoglobin concentration pattern, such as one of the examplesdescribed above, may be calculated, in some embodiments, from images bya computer, such as computer 340 (described below). Optionally, thehemoglobin concentration pattern may be utilized to generate one or morefeature values that are used in a machine learning-based approach by thecomputer for various applications, such as detecting fever, calculatingcore body temperature, detecting intoxication, and/or other applicationsdescribed below. In other embodiments, the hemoglobin concentrationpattern may be utilized to calculate additional values used to representthe extent of facial blood flow and/or extent of vascular dilation,which may be evaluated, e.g., by comparing the extent of blood flowand/or vascular dilation to thresholds in order to detect whether theuser has a fever, estimate core body temperature, detect alcoholintoxication, and/or for other applications described herein.

In one embodiment, a hemoglobin concentration pattern may be convertedto a value representing the proportion of the area in which theintensities of pixels reach a threshold. In one example, the intensitiesbeing evaluated may be average intensities (e.g., average pixelintensities in the images). In another example, the intensities beingevaluated may be maximum intensities corresponding to times of systolicpeaks (e.g., as determined by detecting the spread of a pulse wave inthe area captured in the images, and/or using a reference signal from adifferent source such as a PPG sensor that is not the camera thatcaptured the images).

In another embodiment, a hemoglobin concentration pattern may becompared with one or more reference hemoglobin concentration patternsthat may correspond to specific physiological conditions (e.g., having afever, not having a fever, or a specific core body temperature).Optionally, the reference patterns may be based on previously takenimages of the user, which were taken at times for which the user's corebody temperature was known (e.g., based on a measurement using athermometer). Optionally, similarity of a hemoglobin concentrationpattern to a reference pattern may be utilized to generate one or morefeature values utilized in a machine learning approach, as describedbelow. Optionally, the extent of similarity of a hemoglobinconcentration pattern to a reference pattern may be utilized todetermine whether the user has a certain condition (e.g., fever), asdescribed below.

Various embodiments described herein involve a computer that calculatesa hemoglobin concentration pattern. Optionally, values in a hemoglobinconcentration pattern may be mapped to specific regions on the face,such that the hemoglobin concentration pattern may be considered a layeror grid that can be mapped onto the face in a predetermined manner.

There are various ways in which a hemoglobin concentration pattern maybe calculated in embodiments described herein. Optionally, calculating ahemoglobin concentration pattern involves processing the images, forexample, in order to accentuate the color of one or more channels in theimages, and/or accentuate the changes to colors of one or more channelsin the images (e.g., accentuating color changes caused by blood flowfrom cardiac pulses). Additionally or alternatively, calculating ahemoglobin pattern may involve calculating a representation of thepattern by assigning values to regions in the images and/or to arepresentation of regions on the face. Optionally, the values mayrepresent extents of one or more color channels at the differentregions. Optionally, the values may represent changes to extents of oneor more color channels at the different regions. Optionally, the valuesmay include time series data representing temporal changes to extents ofone or more color channels at each of at least some of the differentregions.

The following are some examples of processing methods that may beapplied to images in order to calculate a hemoglobin concentrationpattern based on images. In some embodiments, one or more of theprocessing methods may be applied by the computer before hemoglobinconcentration patterns are used for calculations and/or detections(e.g., prior to detecting fever, intoxication, and/or estimating corebody temperature). For example, the images may be processed using one ormore of the methods described below, prior to their utilization by thecomputer to calculate hemoglobin concentration patterns used for thecalculations and/or detections. In some embodiments, one or more of theprocessing methods may be applied by the computer as part of thecalculations and/or detections. For example, some layers and/or portionsof a deep learning network used by the computer for the calculationsand/or detections may implement processing operations of the images(which are involved in calculating the hemoglobin concentrationpatterns), while other portions of the deep learning network are used toperform the calculations and/or detections on values representing thehemoglobin concentration patterns.

Various preprocessing approaches may be utilized in order to assist incalculating hemoglobin concentration patterns based on images. Somenon-limiting examples of the preprocessing approaches that may be usedinclude: normalization of pixel intensities (e.g., to obtain a zero-meanunit variance time series signal), and conditioning a time series signalby constructing a square wave, a sine wave, or a user defined shape,such as that obtained from an ECG signal or a PPG signal as described inU.S. Pat. No. 8,617,081, titled “Estimating cardiac pulse recovery frommulti-channel source data via constrained source separation”.Additionally or alternatively, images may undergo various preprocessingto improve the signal, such as color space transformation (e.g.,transforming RGB images into a monochromatic color or images in adifferent color space), blind source separation using algorithms such asindependent component analysis (ICA) or principal component analysis(PCA), and various filtering techniques, such as detrending, bandpassfiltering, and/or continuous wavelet transform (CWT). Variouspreprocessing techniques known in the art that may assist in extractingan iPPG signal from the images are discussed in Zaunseder et al. (2018),“Cardiovascular assessment by imaging photoplethysmography—a review”,Biomedical Engineering 63(5), 617-634. An example of preprocessing thatmay be used in some embodiments is given in U.S. Pat. No. 9,020,185,titled “Systems and methods for non-contact heart rate sensing”, whichdescribes how times-series signals obtained from video of a user can befiltered and processed to separate an underlying pulsing signal by, forexample, using an ICA algorithm.

Another approach that may be utilized as part of preprocessing and/orcalculation of hemoglobin concentration patterns involves Eulerian videomagnification, as described in Wu, Hao-Yu, et al. “Eulerian videomagnification for revealing subtle changes in the world.” ACMtransactions on graphics (TOG) 31.4 (2012): 1-8, and also in thehundreds of references citing this reference. The goal of Eulerian videomagnification is to reveal temporal variations in videos that aredifficult or impossible to see with the naked eye and display them in anindicative manner. This method takes a standard video sequence as input,and applies spatial decomposition, followed by temporal filtering to theframes. The resulting signal is then amplified to reveal hiddeninformation. This method is successfully applied in many applications inorder to visualize the flow of blood as it fills the face and also toamplify and reveal small motions.

In one embodiment, calculating a hemoglobin concentration pattern mayinvolve assigning values to regions on the face and/or in the imagesthat are binary values. FIG. 1a illustrates an example of a hemoglobinconcentration pattern of a sober person where certain regions have avalue “0” because the color of the red channel in the certain regions isbelow a certain threshold, and other regions have a value “1” becausethe color of the red channel in the other regions is above thethreshold. FIG. 1b illustrates an example of the hemoglobinconcentration pattern of the same person when intoxicated, and as aresult the face is redder in certain locations.

In another embodiment, calculating a hemoglobin concentration patternmay involve assigning values to regions on the face and/or in theimages, which are continuous. In one example, in a first hemoglobinconcentration pattern, the pattern may include values in atwo-dimensional grid corresponding to average intensities and/or maximumintensities of colors from one or more channels In another example, in asecond hemoglobin concentration pattern, the pattern may include valuesobtained after processing the images using techniques described hereinfor extracting iPPG signals. Thus, the pattern, in this example, mayinclude values representing statistics of PPG signals at differentregions on the face (e.g., the pattern may include values that are theaverage or maximum of the PPG signals at the different regions). Inanother example, the pattern may include averages of values of certainfiducial points (e.g., systolic peaks and/or dicrotic notches) extractedfrom PPG signals at different regions using iPPG techniques known in theart.

In yet another embodiment, calculating a hemoglobin concentrationpattern may involve assigning values to regions on the face and/or inthe images, which are a time series. In one example, in a hemoglobinconcentration pattern, the pattern may include values in atwo-dimensional grid, where each position in the gird is a time seriesthat represents a shape of a PPG pulse wave at the location on the facecorresponding to the position. Optionally, the time series for theposition may be extracted from images corresponding to multiple pulsewaves (and thus represent a typical PPG shape of a pulse wave atlocation on the face).

FIG. 1c is a schematic illustration of components of a system configuredto detect fever and/or intoxication (e.g., due to alcohol consumption).In one embodiment, the system includes at least first and secondhead-mounted inward-facing cameras 332 that take images 333 ofrespective first and second regions on a user's face. Henceforth, forthe sake of brevity, “the first and second head-mounted inward-facingcameras 332” will be referred to as “the cameras 332”. Optionally, thecameras 332 are physically coupled to a frame of smartglasses 330. Thesystem also includes a computer 340, which may or may not be physicallycoupled to the frame of the smartglasses 330. The system may includeadditional elements such as a movement sensor, an inertial measurementunit (IMU) 342, a skin temperature sensor 343, an environment sensor 344that is configured to measure temperature and/or humidity of theenvironment, and/or a user interface 348.

In one embodiment, the computer 340 calculates, based on baseline imagescaptured with the cameras 332 while the user did not have a fever, abaseline pattern comprising values indicative of first and secondbaseline hemoglobin concentrations at the first and second regions onthe user's face, respectively. Additionally, the computer 340calculates, based on a current set of images captured with the cameras332, a current pattern comprising values indicative of first and secondcurrent hemoglobin concentrations at the first and second regions,respectively. In this embodiment, the computer 340 detects whether theuser has a fever based on a deviation of the current pattern from thebaseline pattern.

In some embodiments, the user is considered to have a fever if theuser's body temperature rises above a predetermined extent beyond thebaseline (“normal”) body temperature for the user. For example, if theuser's body temperature rises by 1.5° C. or more above normal, the useris considered to have a fever. In other embodiments, the user isconsidered to have a fever if the user's body temperature rises above apredetermined threshold (which may or may not be specific to the user,and may or may not depend on the hour of the day because the normaltemperature may be a function of the hour of the day). For example, ifthe user's body temperature rises above 38° C., the user is consideredto have a fever.

In another embodiment, the computer 340 calculates, based on baselineimages captured with the cameras 332 while the user was sober, abaseline pattern comprising values indicative of first and secondbaseline hemoglobin concentrations at the first and second regions onthe user's face, respectively. Additionally, the computer 340calculates, based on a current set of images captured with the cameras332, a current pattern comprising values indicative of first and secondcurrent hemoglobin concentrations at the first and second regions,respectively. In this embodiment, the computer 340 detects whether theuser is intoxicated based on a deviation of the current pattern from thebaseline pattern.

In some embodiments, the user is considered to be intoxicated (fromalcohol) if the user's Blood Alcohol Level (BAC) is above apredetermined threshold. For example, the user may be consideredintoxicated if the BAC is above 0.05%, 0.08%, or 0.1%. In otherembodiments, the user may be considered intoxicated if the user consumedat least a certain amount of alcohol during a preceding window of time.For example, the user may be considered intoxicated if the user consumedat least two standard drinks (e.g., two bottles of beer with 5% alcoholcontent) during a period of two hours or less. In still otherembodiments, the user may be considered intoxicated if the user isassessed to exhibit behavior consistent with intoxication and/or isconsidered unable to care for the safety of oneself or others.

The smartglasses 330 are configured to be worn on a user's head.Optionally, various sensors and/or cameras that are physically coupledto the smartglasses 330, e.g., by being attached to and/or embedded inthe frame of the smartglasses 330, are used to measure the user whilethe user wears the smartglasses 330. Optionally, at least some of thesensors and/or cameras that are physically coupled to the smartglasses330 may be utilized to measure the environment in which the user is in.In one example, the smartglasses 330 are eyeglasses with sensors andelectronics attached thereto and/or embedded therein. In anotherexample, the smartglasses 330 may be an extended reality device (i.e.,an augmented realty device, a virtual reality device, and/or mixedreality device). In some embodiments, the cameras 332 are physicallycoupled to the frame of the smartglasses 330.

Each camera from among the cameras 332 is located less than 10 cm fromthe face of the user (to whose head the cameras are mounted).Additionally, the first and second head-mounted inward-facing cameras332 are configured to capture images of respective first and secondregions on the user's face (i.e., the first camera captures images ofthe first region and the second camera captures images of the secondregion). The first and second regions do not completely overlap.

Having multiple inward-facing head-mounted cameras close to the face canconfer the advantage of covering many regions on the face, while stillhaving an aesthetic head-mounted system (due to the close distances ofthe cameras from the face) and stable and sharp images (due to thecameras capturing the same regions even when the user makes angularmotions). In some embodiments, the cameras 332 may be at closersdistances to the face. In one example, each of the cameras 332 is lessthan 5 cm from the user's face. In another example, each of the cameras332 is less than 2 cm from the user's face.

The locations, orientations, and/or optical properties of the first andsecond head-mounted inward-facing cameras 332 can cause them to captureimages of different respective first and second regions. Optionally,each of the first and second regions contains an area of at least 1 cm²of skin on the user's face. Optionally, each of the first and secondregions contains an area of at least 4 cm² of skin on the user's face.

In some embodiments, the middle of the first region is not at the samelocation as the middle of the second region. In one example, the middlesof the first and second regions are at least 1 cm apart. In anotherexample, the middles of the first and second regions are at least 4 cmapart. In yet another example, the middles of the first and secondregions are at least 8 cm apart.

Herein, the middle of a region is the average co-ordinate of points inthe region (e.g., when points in the region can be described as residingin a two- or three-dimensional space).

In one example, the first region is located above the user's eyes, andthe second region is located below the user's eyes. Optionally, in thisexample, the first and second regions do not overlap. In anotherexample, the middle of the first region is located less than 4 cm fromthe vertical symmetric axis of the user's face, and the middle of thesecond region is located more than 4 cm from the vertical symmetricaxis. Optionally, in this example, the first and second regions dooverlap.

The first and second head-mounted inward-facing cameras 332 are smalland lightweight. In some embodiments, each of the cameras 332 weighsbelow 10 g and even below 2 g. In one example, each of these cameras isa multi-pixel video camera having a CMOS or a CCD sensor. The videocamera may capture images at various rates. In one example, the images333 include images captured at a frame rate of at least 3 frames persecond (fps). In another example, the images 333 include images capturedat a frame rate of at least 30 fps. In still another example, the images333 include images captured at a frame rate of at least 256 fps. Imagestaken by the cameras 332 may have various resolutions. In one example,the images 333 include images that have a resolution of at least 8×8pixels. In another example, the images 333 include images that have aresolution of at least 32×32 pixels. In yet another example, the images333 include images that have a resolution of at least 640×480 pixels.

In some embodiments, at least one of the cameras 332 may capture lightin the near infrared spectrum (NIR). Optionally, such a camera mayinclude optics and sensors that capture light rays in at least one ofthe following NIR spectrum intervals: 700-800 nm, 700-900 nm, 700-1,050nm. Optionally, the sensors may be CCD sensors designed to be sensitivein the NIR spectrum and/or CMOS sensors designed to be sensitive in theNIR spectrum.

In one example, the cameras 332 are mounted between 5 mm and 50 mm awayfrom the user's head. Examples of camera sensors that are sensitive towavelengths below 1050 nm include CCD and CMOS sensors, which aresensitive to wavelengths in at least a portion of the range of 350 nm to1050 nm.

In some embodiments, the system may include an optical emitterconfigured to direct electromagnetic radiation at the first and/orsecond regions. Optionally, the optical emitter comprises one or more ofthe following: a laser diode (LD), a light-emitting diodes (LED), and anorganic light-emitting diode (OLED). It is to be noted that whenembodiments described in this disclosure utilize optical emittersdirected at a region of interest (ROI), such as an area appearing inimages 333, the optical emitter may be positioned in various locationsrelative to the ROI. In some embodiments, the optical emitter may bepositioned essentially directly above the ROI, such that electromagneticradiation is emitted at an angle that is perpendicular (or within 10degrees from being perpendicular) relative to the ROI. Optionally, acamera may be positioned near the optical emitter in order to capturethe reflection of electromagnetic radiation from the ROI. In otherembodiments, the optical emitter may be positioned such that it is notperpendicular to the ROI. Optionally, the optical emitter does notocclude the ROI. In one example, the optical emitter may be located atthe top of a frame of a pair of eyeglasses, and the ROI may include aportion of the forehead. In another example, optical emitter may belocated on an arm of a frame of a pair of eyeglasses and the ROI may belocated above the arm or below it.

Due to the position of the cameras 332 relative to the face, in someembodiments, there may be an acute angle between the optical axis of acamera from among these cameras and the area captured by images taken bysaid camera (e.g., when the camera is fixed to an eyeglasses frame andthe area is on, and/or includes a portion of, the forehead or a cheek).In order to improve the sharpness of images captured by said camera, thecamera may be configured to operate in a way that takes advantage of theScheimpflug principle. In one embodiment, a camera from among thecameras 332 includes a sensor and a lens; the sensor plane is tilted bya fixed angle greater than 2° relative to the lens plane according tothe Scheimpflug principle in order to capture a sharper image when thesmartglasses are worn by the user (where the lens plane refers to aplane that is perpendicular to the optical axis of the lens, which mayinclude one or more lenses). In another embodiment, the camera includesa sensor, a lens, and a motor; the motor tilts the lens relative to thesensor according to the Scheimpflug principle. The tilt improves thesharpness of images when the smartglasses are worn by the user.Additional details regarding the application of the Scheimpflugprinciple are discussed further below.

In some embodiments, the system may include a short-wave infrared (SWIR334) inward-facing head-mounted camera that is configured to detectwavelengths in at least a portion of the range of 700 nm to 2500 nm. Oneexample of a SWIR sensor suitable for this embodiment is Indium GalliumArsenide (inGaAs) sensor. Optionally, the computer 340 is configured todetect whether the user has the fever also based on a deviation of acurrent SWIR pattern from a baseline SWIR pattern taken while the userdid not have a fever. Optionally, the current SWIR pattern is generatedbased on images taken with the SWIR 334 at a current time, while thebaseline SWIR pattern is generated based on SWIR-images 335 taken withthe SWIR 334 during one or more previous periods, while the user did nothave a fever. In some embodiments, at least some of the feature values,described further below, which are generated based on images from amongthe images 333 may be generated for the SWIR-images 335. Thus,SWIR-images 335 may be utilized, in some embodiments, as inputs for adetection of whether the user has a fever and/or is intoxicated.

Variations in the reflected ambient light may introduce artifacts intoimages collected with inward-facing cameras that can add noise to theseimages and make detections and/or calculations based on these imagesless accurate. In some embodiments, the system includes anoutward-facing camera, which is coupled to the smartglasses, and takesimages of the environment. Optionally, this outward-facing camera islocated less than 10 cm from the user's face and weighs below 5 g.Optionally, the outward-facing camera may include optics that provide itwith a wide field of view.

The computer 340 is configured, in some embodiments, to detect a certaincondition (e.g., whether the user has a fever or whether the user isintoxicated) based on a deviation of a current pattern from a baselinepattern.

In different embodiments, a reference to “the computer 340” may refer todifferent components and/or a combination of components. In someembodiments, the computer 340 may include a processor located on ahead-mounted device, such as the smartglasses 330. In other embodiments,at least some of the calculations attributed to the computer 340 may beperformed on a remote processor, such as the user's smartphone and/or acloud-based server. Thus, references to calculations being performed bythe “computer 340” should be interpreted as calculations being performedutilizing one or more computers, with some of these one or morecomputers possibly being of a head-mounted device to which the cameras332 are coupled.

The current pattern is calculated, by the computer 340, based on currentimages, from among the images 333, captured by the cameras 332. Thecurrent images are taken during some times that fall during a windowleading up to a current time. Optionally, the window is at most fivesecond long. Optionally, the window is at most 30 seconds long.Optionally, the window is at most 5 minutes long. Optionally, the windowis at most one hour long. The current images taken during the window areutilized by the computer 340 to calculate the current pattern, which isindicative of at least first and second current hemoglobinconcentrations at the first and second regions, respectively. It is tobe noted that images taken during the window need not be takencontinuously throughout the window, rather they may be takenintermittently or sporadically during the window.

As discussed below, extents of reflection and absorption of lightthrough the skin may depend on the wavelength of the light. Thus, insome embodiments, patterns of hemoglobin concentration may includevalues calculated based on different channels of the same images. Thus,in the detection of fever and/or intoxication, the baseline patternand/or current pattern may include multiple sets of values derived frommultiple different channels.

The baseline pattern is calculated, by the computer 340, based onbaseline images from among the images 333, captured by the cameras 332.Optionally, the baseline images were taken previously (prior to when thecurrent images were taken) while the user was not in a state beingdetected. In one embodiment, in which the computer 340 detects whetherthe user has a fever, the baseline images were taken while the user didnot have a fever. In another embodiment, in which the computer 340detects whether the user is intoxicated, the baseline images were takenwhile the user was sober (or assumed to be sober).

Windows during which baseline images were taken may have differentlengths, and end prior to the current time. Optionally, windows duringwhich baseline images are taken end before the current window begins Inone example, the baseline images may have been taken at different timesduring a window spanning several hours. In another example, the baselineimages include images taken at different times during a window spanningseveral days/weeks/months, such that the baseline images include imagestaken on different days/weeks/months, respectively.

In some embodiments, the computer 340 may receive indications indicativeof times in which the user is in a baseline state (e.g., without a feveror not intoxicated). Optionally, at least some of the baseline imagesare selected based on the indications. For example, images taken by thecameras 332 are included in the baseline images if there is anindication indicating the user was in a baseline state within temporalproximity to when they were taken. In one example, images taken within awindow spanning from an hour before to an hour after a time for whichthere is an indication that the user was in a baseline state, areincluded in the baseline images. In another example, images taken withina window spanning from five minutes before to five minutes after a timefor which there is an indication that the user was in a baseline state,are included in the baseline images. In yet another example, imagestaken within a window spanning from 30 seconds before to 30 secondsafter a time for which there is an indication that the user was in abaseline state, are included in the baseline images.

The indications indicative of the times in which the user is in abaseline state may come from one or more sources. In some embodiments,indications may be self-reported. For example, the user may provideindications indicating when he/she were sober and/or without a fever. Inother embodiments, some other person such as a caregiver, physician,supervisor, and/or guardian may provide such indications. In still otherembodiments, indications may be received from sensors that measure theuser, which are not the cameras 332. In one example, temperaturemeasurements taken by an oral thermometer and/or a non-head-mountedthermal camera are used to determine whether the user has a fever. Inanother example, analysis of the user's movements (e.g., as measured bythe IMU 342) and/or voice patterns (as recorded with microphones) areused to determine whether the user is intoxicated or not (e.g., usingmachine learning methods known in the art).

In some embodiments, images taken in a certain context are assumed to betaken in a baseline state. In one example, images taken in the daytimeat school/work, while the user behaved as expected from a sober/healthyperson, are assumed to be taken while the user was not intoxicatedand/or without a fever. In another example, all images taken while thereis no indication that the user was not in a baseline state, are assumedto be taken in the baseline state. In this example, it may be assumed(for most normative people) that most of the time the user does not havea fever and/or is not intoxicated.

The baseline images are utilized by the computer 340 to calculate thebaseline pattern, which is indicative of at least first and secondbaseline hemoglobin concentrations at the first and second regions,respectively. In one example, the baseline pattern is indicative offirst and second baseline hemoglobin concentrations at the first andsecond regions characteristic of times at which the user does not have afever, or is not intoxicated.

In addition to a baseline pattern indicating hemoglobin concentrationscharacteristic of times in which the user is in a baseline state, insome embodiments, the computer 340 may utilize a detected-state patternindicating hemoglobin concentrations characteristic of times in whichthe user is in the detected state (e.g., has a fever and/or isintoxicated). Optionally, detection of whether the user is in thedetected state is done by a deviation of the current pattern from thedetected-state pattern. Optionally, the detected-state pattern iscalculated by the computer 340 based on additional images, from amongthe images 333, taken at times at which there was an indication that theuser was in a certain state (e.g., had a fever and/or was intoxicated).Optionally, the indications may be self-reported, provided by anotherperson, and/or a result of analysis of sensor measurements, as describedabove.

In one embodiment, the computer 340 calculates, based on additionalbaseline images captured with the cameras 332 while the user had afever, a fever-baseline pattern comprising values indicative of firstand second fever hemoglobin concentrations at the first and secondregions, respectively. The computer 340 then bases the detection ofwhether the user has the fever also on a deviation of the currentpattern from the fever-baseline pattern (in addition to the deviationfrom the baseline pattern).

In one embodiment, the computer 340 calculates, based on additionalbaseline images captured with the cameras 332 while the user wasintoxicated, an intoxication-baseline pattern comprising valuesindicative of first and second intoxication hemoglobin concentrations atthe first and second regions, respectively. The computer 340 then basesthe detection of whether the user is intoxicated also based on adeviation of the current pattern from the intoxication-baseline patternin addition to the deviation from the baseline pattern).

Detection of reflections of light at different wavelengths can be usedto account for thermal interference by the environment. In someembodiments, accounting for thermal inference relies on the followingthree observations:

(i) blue and green wavelengths penetrate the skin less deeply comparedto red and near-infrared wavelengths, (ii) the capillaries are closer tothe skin surface compared to the arterioles, and (iii) the PPG amplitudeis proportional to the skin temperature, probably because both bloodviscosity and vasoconstriction increase with the increase in skintemperature. Additionally, we will examine the ratio R_(depth), which isdefined as followsR _(depth)=reflected light from the capillaries/reflected light from thearterioles

Based on the aforementioned observations, it is to be expected thatR_(depth) will be greater for the blue and green wavelengths compared tothe red and near-infrared wavelengths. This means that temperatureinterference from the environment is expected to influence thehemoglobin concentration pattern derived from the blue and greenwavelengths more than it influences the hemoglobin concentration patternderived from the red and near-infrared wavelengths.

In one embodiment, because environment heating increases vasodilationwhilst environment cooling decreases blood flow to the skin, and becausetemperature interference from the environment is expected to influence ahemoglobin concentration pattern derived from the blue and/or greenwavelengths more than it influences a hemoglobin concentration patternderived from the red and/or near-infrared wavelengths, the system canimprove its accuracy when estimating temperatures, such as the core bodytemperature and/or detecting fever based on the level of discrepancybetween (i) a hemoglobin concentration pattern derived from a firstchannel corresponding to wavelengths mostly below 580 nanometers, suchas the blue and/or green reflections, and (ii) the hemoglobinconcentration pattern derived from a second channel corresponding towavelengths mostly above 580 nanometers, such as the red and/ornear-infrared reflections.

It is noted that most optical filters are not perfect, and the meaningof the sentence “channel corresponding to wavelengths mostly below 580nanometers” is that the filter suppresses red and near-infrared at leasttwice as much, compared to blue and/or green. Similarly, the meaning ofthe sentence “channel corresponding to wavelengths mostly above 580nanometers” is that the filter suppresses blue and green at least twiceas much, compared to red and/or near-infrared.

For example, when a temperature corresponding to hemoglobinconcentration pattern derived from the blue and/or green wavelengths ishigher than a predetermined threshold compared to a temperaturecorresponding to the hemoglobin concentration pattern derived from thered and/or near-infrared wavelengths, this may indicate that the user isin a hot environment (such as being close to a heater or in directsunlight), and/or that the user has just arrived from a hot environment(such as entering a cold building from a hot summer street). In anotherexample, when a temperature corresponding to hemoglobin concentrationpattern derived from the blue and/or green wavelengths is lower than apredetermined threshold from the temperature corresponding to thehemoglobin concentration pattern derived from the red and/ornear-infrared wavelengths, this may indicate that the user is in a coldenvironment, and/or that the user is being exposed to a wind that coolsthe skin.

In one embodiment, the baseline images and the current set of imagescomprise a first channel corresponding to wavelengths that are mostlybelow 580 nanometers and a second channel corresponding to wavelengthsmostly above 580 nanometers; the baseline pattern comprises: (i) firstvalues, derived based on the first channel in the baseline images, whichare indicative of the first and second baseline hemoglobinconcentrations at the first and second regions, respectively, and (ii)second values, derived based on the second channel in the baselineimages, which are indicative of third and fourth baseline hemoglobinconcentrations at the first and second regions, respectively.Optionally, the current pattern comprises: (i) third values, derivedbased on the first channel in the current set of images, which areindicative of the first and second current hemoglobin concentrations atthe first and second regions, respectively, and (ii) fourth values,derived based on the second channel in the current set of images, whichare indicative of third and fourth current hemoglobin concentrations atthe first and second regions, respectively. Having separate values fordifferent wavelengths enables to account for interference from theenvironment when detecting whether the user has the fever becausetemperature interference from the environment is expected to affect thethird values more than it affects the fourth values.

In another embodiment, the baseline images and the current set of imagescomprise a first channel corresponding to wavelengths that are mostlybelow 580 nanometers and a second channel corresponding to wavelengthsmostly above 580 nanometers; the baseline pattern comprises: (i) firstvalues, derived based on the first channel in the baseline images, whichare indicative of the first and second baseline hemoglobinconcentrations at the first and second regions, respectively, and (ii)second values, derived based on the second channel in the baselineimages, which are indicative of third and fourth baseline hemoglobinconcentrations at the first and second regions, respectively. Thecurrent pattern comprises: (i) third values, derived based on the firstchannel in the current set of images, which are indicative of the firstand second current hemoglobin concentrations at the first and secondregions, respectively, and (ii) fourth values, derived based on thesecond channel in the current set of images, which are indicative ofthird and fourth current hemoglobin concentrations at the first andsecond regions, respectively. Optionally, the computer 340 calculates aconfidence in a detection of the fever based on the deviation of thecurrent pattern from the baseline pattern, such that the confidencedecreases as the difference between the third values and the fourthvalues increases. For example, the confidence is a value proportional tosaid deviation. Having separate values for different wavelengths enablesto account for interference from the environment when calculating saidconfidence because temperature interference from the environment isexpected to affect the third values more than it affects the fourthvalues.

In some embodiments, the computer 340 calculates the values indicativeof the baseline and current hemoglobin concentrations based on detectingfacial flushing patterns in the baseline and current images. In oneexample, the facial flushing patterns are calculated based on applyingdecorrelation stretching to the images (such as using a three colorspace), then applying K-means clustering (such as three clusterscorresponding to the three color space), and optionally repeating thedecorrelation stretching using a different color space. In anotherexample, the facial flushing patterns are calculated based on applyingdecorrelation stretching to the images (such as using a three colorspace), and then applying a linear contrast stretch to further expandthe color range.

There are various computational approaches that may be utilized by thecomputer 340 in order to detect whether the user has a fever, and/or isintoxicated, based on a deviation of a current pattern from a baselinepattern.

In one embodiment, the computer 340 calculates a value indicative of amagnitude of the deviation of the current pattern from the baselinepattern. For example, when both patterns include numerical values, thevalues in corresponding regions in both patterns may be subtracted fromeach other. In FIG. 1a and FIG. 1b , this may be interpreted ascalculating the difference in the number of “1”s in both patterns.Optionally, if the subtraction of the baseline pattern from the currentpattern reaches a certain threshold, the user is assumed to be in acertain state (e.g., has a fever and/or is intoxicated).

In another embodiment, the computer 340 calculates a value indicative ofthe deviation between the current pattern and the baseline pattern basedon vector representations of the patterns. For example, if each of thepatterns may be represented as a vector in a multi-dimensional space,the deviation may be calculated using one or more techniques known inthe art for calculating distances between vectors, such as a dotproduct, Euclidean distance, or a distance according to some other norm.Optionally, if the distance has at least a certain value, the user isassumed to be in a certain state (e.g., has a fever and/or isintoxicated).

In yet another embodiment, if the difference between a vectorrepresentation of the current pattern and a vector representation of thebaseline pattern is in a certain direction, the computer 340 detects theuser is in a certain state corresponding to the certain direction. Forexample, there may be a predetermined direction of change for patternswhen the user becomes intoxicated.

In still another embodiment, in which the current pattern and thebaseline pattern include time series data, the computer 340 utilizesmethods known in the art for comparison of time series, such as dynamictime warping, in order to calculate an extent of deviation of thecurrent pattern from the baseline pattern.

In some embodiments, the computer 340 may utilize the fever-baselinepattern and/or the intoxication-baseline pattern, which are discussedabove, in order to detect whether the user has a fever and/or isintoxicated. In one example, if the current pattern is more similar tothe fever-baseline pattern than it is to the baseline pattern, thecomputer 340 detect the user has a fever. In another example, if a firstdifference between the current pattern and the intoxication-baselinepattern is below a first threshold, while a second difference betweenthe current pattern and the baseline pattern is above a secondthreshold, the computer 340 detects the user is intoxicated.

Detection of whether the user has a fever and/or is intoxicated mayinvolve utilization of machine learning approaches. In some embodiments,baseline images and/or current images may be utilized by the computer340 to generate feature values that are used in a machine learning-basedapproach by the computer 340 to detect whether the user has a feverand/or is intoxicated. In some embodiments, the computer 340 calculatesthe feature values based on data that includes at least some of theimages 333 (and possibly other data) and utilizes a model 346 tocalculate, based on the feature values, a value indicative of whetherthe user has a fever and/or a value indicative of whether the user isintoxicated.

In one embodiment, the value calculated by the computer 340 based on thefeature values is a binary value. For example, the value is “1” if theuser has a fever and “0” if the user does not have a fever. In someembodiments, the value calculated by the computer 340 based on thefeature values is a scalar. For example, the calculated value may be anestimation of the user's core body temperature and/or an estimation ofthe increase of the user's core body temperature. In such embodiments,if the calculated value reaches a threshold, the user is considered tohave a fever. In a similar fashion, a binary value may be calculated inthe case of intoxication, and detecting intoxication may be done if acalculated value indicative of an intoxication level of the user reachesa predetermined threshold.

Generally, machine learning-based approaches utilized by embodimentsdescribed herein involve training a model on samples, with each sampleincluding: feature values generated based on images taken by the cameras332, and optionally other data, which were taken during a certainperiod, and a label indicative of an extent of fever and/or intoxication(during the certain period). Optionally, a label may be providedmanually by the user and/or other sources described above as providingindications about the state of the user (e.g., indications of a feverlevel and/or intoxication level, described above). Optionally, a labelmay be extracted based on analysis of electronic health records of theuser, generated while being monitored at a medical facility.

In some embodiments, the model 346 may be personalized for a user bytraining the model on samples that include: feature values generatedbased on measurements of the user, and corresponding labels indicativeof the extent of fever and/or intoxication of the user while themeasurements were taken. In some embodiments, the model 346 may begenerated based on measurements of multiple users, in which case, themodel 346 may be considered a general model. Optionally, a modelgenerated based on measurements of multiple users may be personalizedfor a certain user by being retrained on samples generated based onmeasurements of the certain user.

There are various types of feature values that may be generated by thecomputer 340 based on input data, which may be utilized to calculate avalue indicative of whether the user has a fever and/or is intoxicated.Some examples of feature values include “raw” or minimally processedvalues based on the input data (i.e., the features are the data itselfor applying generic preprocessing functions to the data). Other examplesof feature values include feature values that are based on higher-levelprocessing, such a feature values determined based on domain-knowledge.In one example, feature values may include values of the patternsthemselves, such as values included in the current pattern, the baselinepattern, the fever-baseline pattern, and/or the intoxication-baselinepattern. In another example, feature values may include values that arefunctions of patterns, such as values that represent a deviation of thecurrent pattern from the baseline pattern.

In one non-limiting example, feature values generated by the computer340 include pixel values from the images 333. In another non-limitingexample, feature values generated by the computer 340 include timingsand intensities corresponding to fiducial points identified in iPPGsignals extracted from the images 333. In yet another non-limitingexample, feature values generated by the computer 340 include binaryvalues representing the baseline pattern and the current pattern.

The following are some examples of the various types of feature valuesthat may be generated based on images from among the images 333 by thecomputer 340. In one embodiment, one or more of the feature values maybe various low-level features derived from images, such as featuresgenerated using Gabor filters, local binary patterns (LBP) and theirderivatives, algorithms such as SIFT and/or SURF (and theirderivatives), image keypoints, histograms of oriented gradients (HOG)descriptors, and products of statistical procedures such independentcomponent analysis (ICA), principal component analysis (PCA), or lineardiscriminant analysis (LDA). Optionally, one or more of the featurevalues may derived from multiple images taken at different times, suchas volume local binary patterns (VLBP), cuboids, and/or opticalstrain-based features. In one example, one or more of the feature valuesmay represent a difference between values of pixels at one time t at acertain location on the face and values of pixels at a differentlocation at some other time t+x (which can help detect different arrivaltimes of a pulse wave).

In some embodiments, at least some feature values utilized by thecomputer 340 describe properties of the cardiac waveform in an iPPGsignal derived from images from among the images 333. To this end, thecomputer 340 may employ various approaches known in the art to identifylandmarks in a cardiac waveform (e.g., systolic peaks, diastolic peaks),and/or extract various types of known values that may be derived fromthe cardiac waveform, as described in the following examples.

In one embodiment, at least some of the feature values generated basedon the iPPG signal may be indicative of waveform properties thatinclude: systolic-upstroke time, diastolic time, and the time delaybetween the systolic and diastolic peaks, as described in Samria, Rohan,et al. “Noninvasive cuffless estimation of blood pressure usingPhotoplethysmography without electrocardiograph measurement.” 2014 IEEEREGION 10 SYMPOSIUM. IEEE, 2014.

In another embodiment, at least some of the feature values generatedbased on the iPPG signal may be derived from another analysis approachto PPG waveforms, as described in US Patent Application US20180206733,entitled “Device, method and system for monitoring and management ofchanges in hemodynamic parameters”. This approach assumes the cardiacwaveform has the following structure: a minimum/starting point (A),which increases to a systolic peak (B), which decreases to a dicroticnotch (C), which increases to a dicrotic wave (D), which decreases tothe starting point of the next pulse wave (E). Various features that maybe calculated by the computer 340, which are suggested in theaforementioned publication, include: value of A, value of B, value of C,value of D, value of E, systol area that is the area under ABCE, diastolarea that is the area under CDE, and the ratio between BC and DC.

In still another embodiment, the computer 340 may utilize the variousapproaches described in Elgendi, M. (2012), “On the analysis offingertip photoplethysmogram signals”, Current cardiology reviews, 8(1),14-25, in order to generate at least some of the feature values bases onthe iPPG signal. This reference surveys several preprocessing approachesfor PPG signals as well as a variety of feature values that may beutilized. Some of the techniques described therein, which may beutilized by the computer 340, include calculating feature values basedon first and second derivatives of PPG signals.

In some embodiments, at least some of the feature values may representcalibration values of a user, which are values of certain parameterssuch as waveform properties described above when the user had a knownstate (e.g., while it was known that the user was without a fever and/orsober).

In some embodiments, the computer 340 may utilize one or more featurevalues indicative of the user's heart rate. Optionally, these featurevalues may be derived from images from among the images 333, e.g., byperforming calculations on iPPG signals extracted from the images. Inone example, a time series signal is generated from video images of asubject's exposed skin, and a reference signal is used to perform aconstrained source separation (which is a variant of ICA) on the timeseries signals to obtain the PPG signal; peak-to-peak pulse points aredetected in the PPG signal, which may be analyzed to determineparameters such as heart rate, heart rate variability, and/or to obtainpeak-to-peak pulse dynamics.

In some embodiments, one or more of the feature values utilized by thecomputer 340 to calculate a value indicative of whether the user has afever and/or is intoxicated may be generated based on additional inputsfrom sources other than the cameras 332.

Stress is a factor that can influence the diameter of the arteries, andthus influence the blood flow and resulting hemoglobin concentrationpatterns. In one embodiment, the computer 340 is further configured to:receive a value indicative of a stress level of the user, and generateat least one of the feature values based on the received value.Optionally, the value indicative of the stress level is obtained using athermal camera. In one example, the system may include an inward-facinghead-mounted thermal camera configured to take measurements of aperiorbital region of the user, where the measurements of a periorbitalregion of the user are indicative of the stress level of the user. Inanother example, the system includes an inward-facing head-mountedthermal camera configured to take measurements of a region on theforehead of the user, where the measurements of the region on theforehead of the user are indicative of the stress level of the user. Instill another example, the system includes an inward-facing head-mountedthermal camera configured to take measurements of a region on the noseof the user, where the measurements of the region on the nose of theuser are indicative of the stress level of the user.

Hydration is a factor that affects blood viscosity, which can affect thespeed at which blood flows in the body, and consequently may affectblood flow and hemoglobin concentration patterns. In one embodiment, thecomputer 340 is further configured to: receive a value indicative of ahydration level of the user, and generate at least one of the featurevalues based on the received value. Optionally, the system includes anadditional camera configured to detect intensity of radiation that isreflected from a region of exposed skin of the user, where the radiationis in spectral wavelengths chosen to be preferentially absorbed bytissue water. In one example, said wavelengths are chosen from threeprimary bands of wavelengths of approximately 1100-1350 nm,approximately 1500-1800 nm, and approximately 2000-2300 nm. Optionally,measurements of the additional camera are utilized by the computer 340as values indicative of the hydration level of the user.

Momentary physical activity can affect the blood flow of the user (e.g.,due to the increase in the heart rate that it involves). In order toaccount for this factor, in some embodiments, the computer 340 maygenerate one or more feature values representing the extent of theuser's movement from measurements of the IMU 342.

The user's skin temperature may affect blood viscosity, thus it mayinfluence facial hemoglobin concentration patterns. Some embodiments mayinclude the skin temperature sensor 343, which may be a head-mountedsensor. The skin temperature sensor 343 measures temperature of a regioncomprising skin on the user's head (T_(skin)). Optionally, the computer340 generates one or more feature values based on T_(skin), such asfeature values indicating average skin temperature or a difference frombaseline skin temperature.

The temperature and/or humidity in the environment may also be a factorthat is considered in some embodiments. The temperature and/or humiditylevel in the environment can both impact the user's skin temperature andcause a physiologic response involved in regulating the user's bodytemperature, which may affect observed hemoglobin concentrationpatterns. Some embodiments may include the environment sensor 344, whichmay optionally, be head-mounted (e.g., physically coupled tosmartglasses). The environment sensor 344 measures an environmentaltemperature and/or humidity. In one embodiment, the computer 340 maygenerate one or more feature values based on the temperature and/orhumidity in the environment, such as feature values indicating averageenvironment temperature and/or humidity, maximal environment temperatureand/or humidity, or a difference from baseline environment temperatureand/or humidity.

Training the model 346 may involve utilization of various trainingalgorithms known in the art (e.g., algorithms for training neuralnetworks, and/or other approaches described herein). After the model 346is trained, feature values may be generated for certain measurements ofthe user (e.g., current images and baseline images), for which the valueof the corresponding label (e.g., whether the user has a fever and/orwhether the user is intoxicated) is unknown. The computer 340 canutilize the model 346 to calculate a value indicative of whether theuser has a fever and/or whether the user is intoxicated, based on thesefeature values.

In some embodiments, the model 346 may be generated based on data thatincludes measurements of the user (i.e., data that includes images takenby the cameras 332). Additionally or alternatively, in some embodiments,the model 346 may be generated based on data that includes measurementsof one or more other users (such as users of different ages, weights,sexes, body masses, and health states). In order to achieve a robustmodel, which may be useful for detecting fever and/or intoxication invarious conditions, in some embodiments, the samples used to train themodel 346 may include samples based on measurements taken in differentconditions. Optionally, the samples are generated based on measurementstaken on different days, while in different locations, and/or whiledifferent environmental conditions persisted. In a first example, themodel 346 is trained on samples generated from a first set ofmeasurements taken while the user was indoors and not in directsunlight, and is also trained on other samples generated from a secondset of measurements taken while the user was outdoors, in directsunlight. In a second example, the model 346 is trained on samplesgenerated from a first set of measurements taken during daytime, and isalso trained on other samples generated from a second set ofmeasurements taken during nighttime. In a third example, the model 346is trained on samples generated from a first set of measurements takenwhile the user was moving, and is also trained on other samplesgenerated from a second set of measurements taken while the user wassitting.

Utilizing the model 346 to detect whether the user has a fever and/orwhether the user is intoxicated may involve computer 340 performingvarious operations, depending on the type of model. The following aresome examples of various possibilities for the model 346 and the type ofcalculations that may be accordingly performed by the computer 340, insome embodiments, in order to detect whether the user has a fever and/orwhether the user is intoxicated: (a) the model 346 comprises parametersof a decision tree. Optionally, the computer 340 simulates a traversalalong a path in the decision tree, determining which branches to takebased on the feature values. A value indicative of whether the user hasa fever and/or whether the user is intoxicated may be obtained at theleaf node and/or based on calculations involving values on nodes and/oredges along the path; (b) the model 346 comprises parameters of aregression model (e.g., regression coefficients in a linear regressionmodel or a logistic regression model). Optionally, the computer 340multiplies the feature values (which may be considered a regressor) withthe parameters of the regression model in order to obtain the valueindicative of whether the user has a fever and/or whether the user isintoxicated; and/or (c) the model 346 comprises parameters of a neuralnetwork. For example, the parameters may include values defining atleast the following: (i) an interconnection pattern between differentlayers of neurons, (ii) weights of the interconnections, and (iii)activation functions that convert each neuron's weighted input to itsoutput activation. Optionally, the computer 340 provides the featurevalues as inputs to the neural network, computes the values of thevarious activation functions and propagates values between layers, andobtains an output from the network, which is the value indicative ofwhether the user has a fever and/or whether the user is intoxicated.

In some embodiments, a machine learning approach that may be applied tocalculating a value indicative of whether the user has a fever and/orwhether the user is intoxicated based on images may be characterized as“deep learning”. In one embodiment, the model 346 may include parametersdescribing multiple hidden layers of a neural network. Optionally, themodel may include a convolution neural network (CNN). In one example,the CNN may be utilized to identify certain patterns in the videoimages, such as hemoglobin concentration patterns. Due to the fact thatcalculations are performed on sequences images display a certain patternof change over time (i.e., across multiple frames), these calculationsmay involve retaining state information that is based on previous imagesin the sequence. Optionally, the model may include parameters thatdescribe an architecture that supports such a capability. In oneexample, the model may include parameters of a recurrent neural network(RNN), which is a connectionist model that captures the dynamics ofsequences of samples via cycles in the network's nodes. This enablesRNNs to retain a state that can represent information from anarbitrarily long context window. In one example, the RNN may beimplemented using a long short-term memory (LSTM) architecture. Inanother example, the RNN may be implemented using a bidirectionalrecurrent neural network architecture (BRNN).

In addition to detecting whether the user has a fever and/or whether theuser is intoxicated, in some embodiments, the computer 340 may utilizethe images 333 to detect additional physiological signals and/orconditions.

In one embodiment, the computer 340 calculates, based on the current setof images, a current heart rate and/or a current respiration rate of theuser. For example, the computer 340 may utilize one or more techniquesdescribed herein or known in the art for calculating heart rate and/orrespiration from iPPG signals. The computer 340 can then utilize thecurrent heart rate and/or the current respiration rate of the user, todetect whether the user has a fever, and other conditions such ashyperthermia or hypothermia, also based on deviations of the currentheart rate and/or the current respiration rate from a baseline heartrate and/or baseline respiration rate of the user, respectively. In oneexample, the computer 340 may utilize a machine learning approachsimilar to the one described above, but instead of using the model 346,the computer 340 uses a different model trained with labelscorresponding to extents of hyperthermia or hypothermia (using featurevalues similar to the ones described above with respect to the model346).

In another embodiment, the computer 340 may utilize one or morecalibration measurements of the user's core body temperature, taken by adifferent device (e.g., a thermometer), prior to a certain time, tocalculate the user's core body temperature based on a certain set ofimages that were taken by the cameras 332 after the certain time. Forexample, the computer 340 may utilize a model trained similarly to themodel 346, but also includes feature values describing patterns observedfor known core body temperatures. In another example, the calibrationmeasurements can be used to adjust values predicted by the computer 340when making estimations of the extent of fever using the model 346.

In one embodiment, the computer 340 calculates the user's core bodytemperature based on the deviation of the current pattern from thebaseline pattern. For example, in this embodiment, the model 346 istrained with labels that are indicative of the user's core bodytemperature.

In yet another embodiment, the computer 340 may detect blushing based onthe deviation of the current pattern from the baseline pattern. In oneexample, the computer 340 may utilize a machine learning approachsimilar to the one described above, but instead of using the model 346,the computer 340 uses a different model trained with labelscorresponding to extents of blushing by the user. In this example,blushing may be identified using image analysis techniques known in theart.

The user interface 348 may be utilized to present values calculated bythe computer 340, such as indications whether the user has a fever orwhether the user is intoxicated. Optionally, the user interface 348 is acomponent of a device of the user, such as a smartphone's screen or anaugmented reality display.

In one embodiment, the computer 340 detects blushing based on thedeviation of the current pattern from the baseline pattern, and presentsan alert to the user about the blushing via the user interface 348. Inone example, the computer 340 provides a biofeedback for the user toenable the user to learn to control the blushing. Optionally, thebiofeedback updates the user about the level of blushing in real time,and by that increases the awareness of the user to the blushing andgradually enables the user to learn to control his/her blushing.

In another embodiment, the computer 340 enables the user to share anintoxication history of the user, upon receiving a permission from theuser. For example, the user may be able to decide to share his/herintoxication history with a certain person in order to increase thetrust, or not share his/her intoxication history with the certain personif the certain persons does not share in return his/her intoxicationhistory with the user.

The following is an additional embodiment of a system configured todetect alcohol intoxication. This embodiment includes memory, acommunication interface, and a processor. The processor is configuredto: receive baseline images of a user's face, captured by a video camerawhile the user was sober; calculate, based on the baseline images, abaseline hemoglobin concentration pattern comprising at least 3 pointsindicative of hemoglobin concentrations on the user's face; receivecurrent images of the user; calculate, based on the current images, acurrent hemoglobin concentration pattern comprising the at least 3points indicative of hemoglobin concentrations on the user's face; anddetect whether the user is intoxicated based on a deviation of thecurrent hemoglobin concentration pattern from the baseline hemoglobinconcentration pattern. Optionally, the video camera is an inward-facinghead-mounted camera (HCAM) that is mounted more than 5 mm away from theuser's head and is sensitive to wavelengths below 1050 nanometer.

The following method for detecting fever may be used by systems modeledaccording to FIG. 1c . The steps described below may be performed byrunning a computer program having instructions for implementing themethod. Optionally, the instructions may be stored on acomputer-readable medium, which may optionally be a non-transitorycomputer-readable medium. In response to execution by a system includinga processor and memory, the instructions cause the system to perform thefollowing steps:

In Step 1, receiving, from first and second inward-facing head-mountedcameras (Cam_(1&2)) sensitive to wavelengths below 1050 nanometer,images of respective first and second regions on a user's face.Optionally, the middles of the first and second regions are at least 4cm apart.

In Step 2, calculating, based on baseline images captured with Cam_(1&2)while the user did not have a fever, a baseline pattern comprisingvalues indicative of first and second baseline hemoglobin concentrationsat the first and second regions, respectively.

In Step 3, calculating, based on a current set of images captured withCam_(1&2), a current pattern comprising values indicative of first andsecond current hemoglobin concentrations at the first and secondregions, respectively.

And in Step 4, detecting whether the user has a fever based on adeviation of the current pattern from the baseline pattern.

In one embodiment, the method for detecting fever optionally includesthe following steps: calculating, based on additional baseline imagescaptured with Cam_(1&2) while the user had a fever, a fever-baselinepattern comprising values indicative of first and second feverhemoglobin concentrations at the first and second regions, respectively;and basing the detecting of whether the user has the fever also on adeviation of the current pattern from the fever-baseline pattern.

In one embodiment, the baseline images and the current set of imagescomprise a first channel corresponding to wavelengths that are mostlybelow 580 nanometers and a second channel corresponding to wavelengthsmostly above 580 nanometers; the baseline pattern comprises: (i) firstvalues, derived based on the first channel in the baseline images, whichare indicative of the first and second baseline hemoglobinconcentrations at the first and second regions, respectively, and (ii)second values, derived based on the second channel in the baselineimages, which are indicative of third and fourth baseline hemoglobinconcentrations at the first and second regions, respectively. Thecurrent pattern comprises: (i) third values, derived based on the firstchannel in the current set of images, which are indicative of the firstand second current hemoglobin concentrations at the first and secondregions, respectively, and (ii) fourth values, derived based on thesecond channel in the current set of images, which are indicative ofthird and fourth current hemoglobin concentrations at the first andsecond regions, respectively. Optionally, method for detecting feverincludes a step of calculating a confidence in a detection of the feverbased on the deviation of the current pattern from the baseline pattern,such that the confidence decreases as the difference between the thirdvalues and the fourth values increases.

FIG. 2a illustrates an embodiment of a system that utilizes multiple PPGsignals, measured using different types of sensors, to detect aphysiological response. In one embodiment, the system includes at leasta head-mounted contact PPG device 782, a camera 784, and a computer 780.

In one embodiment, the head-mounted contact PPG device 782 (alsoreferred to as PPG device 782) measures a signal indicative of a PPGsignal 783 at a first region of interest (ROI₁) on a user's body (alsoreferred to as PPG signal 783). ROI₁ includes a region of exposed skinon the user's head, and the PPG device 782 includes one or more lightsources configured to illuminate ROI₁. For example, the one or morelight sources may include light emitting diodes (LEDs) that illuminateROI₁. Optionally, the one or more LEDs include at least two LEDs,wherein each illuminates ROI₁ with light at a different wavelength. Inone example, the at least two LEDs include a first LED that illuminatesROI₁ with green light and a second LED that illuminates ROI₁ with aninfrared light. Optionally, the PPG device 782 includes one or morephotodetectors configured to detect extents of reflections from ROI₁.

The camera 784 captures images 785 of a second region of interest (ROI₂)on the user's head. The camera is located more than 10 mm away from theuser's head. Optionally, the camera is located more than 20 mm away fromthe user's head. Optionally, the camera 784 is a head-mounted camera. Insome embodiments, the camera 784 may utilize a light source toilluminate ROI₂. Optionally, the light source is configured toilluminate at least a portion of ROI₂, and the camera 784 is locatedmore than 15 cm away from the user's head.

In another embodiment, the system illustrated in FIG. 2a does notinclude a light source that illuminates ROI₂ and the camera 784 may beconsidered a “passive” camera.

In some embodiments, the camera 784 is not head-mounted. Optionally, theimages 785 taken by the non-head mounted camera are synchronized withthe PPG signal 783 (e.g., based on synchronizing the clocks of the PPGdevice 782 and the camera 784, and/or based on time stamps added by thePPG device 782 and time stamps added by the camera 784). Optionally, thesystem achieves data synchronization that is better than 35 millisecondsbetween the PPG signal and the iPPG signals. Optionally, the systemachieves data synchronization better than 1 millisecond between the PPGsignal and the iPPG signals. Examples of cameras that are nothead-mounted, which may be synchronized with the head-mounted PPG device782 include: a smartphone camera, a tablet camera, a laptop camera,and/or webcam.

In some embodiments, references to the camera 784 involve more than onecamera. Optionally, the camera 784 may refer to two or moreinward-facing head-mounted cameras, and ROI₂ includes two or moreregions on the user's head that are respectively captured by the two ormore inward-facing head-mounted cameras. Optionally, the two or moreregions include regions on different sides of the user's head.

Optionally, ROI₂ covers a larger area of exposed skin than ROI₁. In oneexample, the area of ROI₂ is at least ten times larger than the area ofROI₁. In one example, the PPG device 782 does not obstruct the field ofview of the camera 784 to ROI₂.

In some embodiments, various devices, such as the PPG device 782, thecamera 784, and/or the computer 780, may be physically coupled to aframe of smartglasses or to a smart-helmet, which is designed to measurethe user in day-to-day activities, over a duration of weeks, months,and/or years. FIG. 2b and FIG. 2c illustrate smartglasses that may beutilized to realize the invention described herein.

FIG. 2b illustrates smartglasses that include camera 796 and severalcontact PPG devices. The contact PPG devices correspond to the PPGdevice 782 and are used to measure the PPG signal 783. The contact PPGdevices may be coupled at various locations on the frame 794, and thusmay come in contact with various regions on the user's head. Forexample, contact PPG device 791 a is located on the right temple tip,which brings it to contact with a region behind the user's ear (when theuser wears the smartglasses). Contact PPG device 791 b is located on theright temple of the frame 794, which puts it in contact with a region onthe user's right temple (when wearing the smartglasses). It is to benoted that in some embodiments, in order to bring the contact PPG deviceclose such that it touches the skin, various apparatuses may beutilized, such as spacers (e.g., made from rubber or plastic), and/oradjustable inserts that can help bridge a possible gap between theframe's temple and the user's face. Such an apparatus is spacer 792which brings contact PPG device 791 b in contact with the user's templewhen the user wears the smartglasses. Another possible location for acontact PPG device is the nose bridge, as contact PPG device 791 c isillustrated in the figure. It is to be noted the contact PPG device 791c may be embedded in the nose bridge (or one of its components), and/orphysically coupled to a part of the nose bridge.

FIG. 2c illustrates smartglasses that include at least first, second,and third inward-facing cameras, each of which may correspond to thecamera 784. The figure illustrates a frame 797 to which a firstinward-facing camera 795 a is coupled above the lens that is in front ofthe right eye, and a second inward-facing camera 795 b that is coupledto the frame 797 above the lens that is in front of the left eye. Thefigure also illustrates a third inward facing camera 795 c that is onthe left side of the frame 797 and configured to capture images of lowerportions of the user's face (e.g., portions of the left cheek).

The computer 780 is configured, in some embodiments, to detect aphysiological response based on: (i) imaging photoplethysmogram signals(iPPG signals) recognizable in the images 785, and (ii) correlationsbetween the PPG signal 783 and the iPPG signals. Some examples ofphysiological responses that may be detected include: an allergicreaction, a stroke, a migraine, stress, a certain emotional response,pain, and blood pressure (i.e., calculating the blood pressure value).Optionally, the computer 780 forwards an indication of a detection ofthe physiological response 789 to a device of the user and/or to anothercomputer system. Examples of computers that may be utilized to performthis detection are computer 400 or computer 410 illustrated in FIG. 57aand FIG. 57b , respectively.

Herein, sentences of the form “iPPG signal is recognizable in images”refer to effects of blood volume changes due to pulse waves that may beextracted from a series of images of the region. These changes maymanifest as color changes to certain regions (pixels) in the images, andmay be identified and/or utilized by a computer (e.g., in order togenerate a signal indicative of the blood volume at the region).However, these changes need not necessarily be recognizable to the nakedeye (e.g., because of their subtlety, the short duration in which theyoccur, or involvement of light outside of the visible spectrum). Forexample, blood flow may cause facial skin color changes (FSCC) thatcorresponds to different concentrations of oxidized hemoglobin due tovarying volume of blood at a certain region due to different stages of acardiac pulse, and/or the different magnitudes of cardiac output.

Herein, detecting the physiological response may mean detecting that theuser is experiencing the physiological response, and/or that there is anonset of the physiological response. In the case of the physiologicalresponse being associated with one or more values (e.g., bloodpressure), detecting the physiological response may mean calculating theone or more values.

In some embodiments, detecting the physiological response may involvecalculating one or more of the following values: an indication ofwhether or not the user is experiencing the physiological response(e.g., whether or not the user is having a stroke), a value indicativeof an extent to which the user is experiencing the physiologicalresponse (e.g., a level of pain or stress felt by the user), a durationsince the onset of the physiological response (a duration since amigraine has started), and a duration until an onset of thephysiological response.

In some embodiments, the computer 780 detects the physiological responseutilizing previously taken PPG signals of the user (taken with the PPGdevice 782) and/or previously taken images (taken with the camera) inwhich previous iPPG signals are recognizable Having such previous valuescan assist the computer 780 to detect changes to blood flow that may beindicative of certain physiological responses. In some embodiments,previously taken PPG signals and/or images are used to generate baselinevalues representing baseline properties of the user's blood flow.Optionally, calculating the baseline values may be done based onpreviously taken PPG signals and/or images that were measured at leastan hour before taking the PPG signal 783 and/or the images 785.Optionally, calculating the baseline values may be done based onpreviously taken PPG signals and/or images that were measured at least aday before the PPG signal 783 and/or the images 785. Some examples ofbaseline values may include baseline physiological signal values (e.g.,baseline heart rate, blood pressure, or heart rate variability). Otherexamples of baseline values may include typical values of fiducialpoints in PPG signals (e.g., magnitudes of systolic peaks) and/ortypical relationships between different fiducial points (e.g., typicaldistance between systolic peaks and dicrotic notches, and the like).

A baseline value may be calculated in various ways. In a first example,the baseline is a function of the average measurements of the user(which include previously taken PPG signals and/or iPPG signalsrecognizable in previously taken images described above). In a secondexample, the baseline value may be a function of the situation the useris in, such that previous measurements taken during similar situationsare weighted higher than previous measurements taken during less similarsituations. A PPG signal may show different characteristics in differentsituations because of the different mental and/or physiological statesof the user in the different situations. As a result, asituation-dependent baseline can improve the accuracy of detecting thephysiological response. In a third example, the baseline value may be afunction of an intake of some substances (such as food, beverage,medications, and/or drugs), such that previous measurements taken afterconsuming similar substances are weighted higher than previousmeasurements taken after not consuming the similar substances, and/orafter consuming less similar substances. A PPG signal may show differentcharacteristics after the user consumes different substances because ofthe different mental and/or physiological states the user may enterafter consuming the substances, especially when the substances includethings such as medications, drugs, alcohol, and/or certain types offood. As a result, a substance-dependent baseline can improve theaccuracy of detecting the physiological response.

There are various ways in which the computer 780 may utilizecorrelations between the PPG signal 783 and the iPPG signals to detectthe physiological response. In some embodiments, the computer 780 mayrely on the fact that due to the proximity of ROI₁ and ROI₂ (both beingon the head and consequently, close by) the appearances of pulse wavesat the different ROIs is highly correlated. This fact may be utilized bythe computer 780 to identify fiducial points in the PPG signal 783,which is often a strong signal, and then to identify the correspondingfiducial points in the correlated iPPG signals (that are noisier thanthe PPG signal). Additionally or alternatively, when a using machinelearning-based approach, at least some of the feature values used by thecomputer 780 may reflect values related to correlations between the PPGsignal 783 and the iPPG signals (e.g., values of similarity and/oroffsets between the PPG signal 783 and the iPPG signals). Both uses ofcorrelations are elaborated on further below.

It is to be noted that because the PPG device 782 touches and occludesROI₁, while the camera 784 does not occlude ROI₂, the PPG signal 783extracted from the PPG device 782 usually has a much bettersignal-to-noise (SNR) compared to the iPPG signals extracted from theimages 785 of ROI₂. In addition, due to the shorter distance between thePPG device 782 and ROI₁, and especially in embodiments where the camera784 is a passive camera (i.e., does not include a light source toilluminate ROI₂), the PPG signal 783 will typically suffer much lessfrom illumination changes compared to the iPPG signals.

Furthermore, because both ROI₁ and ROI₂ are on the user's head, andbecause the PPG device 782 and the camera 784 measure the useressentially simultaneously, manifestation of the pulse arrival in thePPG signal 783 and the iPPG signals are typically highly correlated(e.g., the signals exhibit highly correlated pulse arrival times). Thiscorrelation enables the computer 780 to utilize pulse fiducial pointsidentified in the PPG signal 783 (which is less noisy than the iPPGsignals) to extract information from iPPG signals more efficiently andaccurately.

In one embodiment, the computer 780 extracts from the PPG signal 783 oneor more values that may serve as a basis to correlate between the PPGsignal 783 and the iPPG signals. Optionally, the extracted values areindicative of one or more of the following PPG waveform fiducial points:a systolic peak, a dicrotic notch, a diastolic peak. Optionally, theextracted values may be indicative of a timing of a certain fiducialpoint (i.e., when it manifests in the PPG signal 783), and/or themagnitude of the PPG signal 783 at the time corresponding to the certainfiducial point. Additionally or alternatively, the extracted values maybe indicative of other waveform properties such as an interbeatinterval, and a systolic-diastolic peak-to-peak time.

Due to the camera 784 not being in contact with ROI₂, it is often thecase that direct identification of the fiducial points in the iPPGsignals may be difficult, e.g., due to the excessive noise in the signalbecause of movements and ambient light. Knowing an identification offiducial points in the PPG signal 783, such as times of systolic peaks,dicrotic notches, and diastolic peaks, provides useful information fordetermining when these events are to be expected to manifest in the iPPGsignals. The timings of the occurrences of these fiducial points in thePPG signal 783 can serve as a basis according to which fiducial pointscan be determined in the iPPG signals.

In one embodiment, times corresponding to fiducial points, as determinedbased on the PPG signal 783, are also used for fiducial points in theiPPG signals. Thus, the magnitudes of the fiducial points in the iPPGsignals are taken essentially at the same times of the fiducial pointsin the PPG signal 783. Such an approach can be especially accurate whenROI₁ and ROI₂ are close to each other, thus it is likely thatmanifestation of pulse waves occurs at very similar times in ROI₁ andROI₂, so when, for example, there is a systolic peak in the PPG signal863, there is also one approximately at the same time in the iPPGsignals.

In another embodiment, times corresponding to fiducial points, asdetermined based on the PPG signal 783, may also be used to determinefiducial points in the iPPG signals, by applying a certain offset to thetimes. This certain offset may be used to account for the differencebetween the distances/route blood travels in order to reach ROI₂ asopposed to the distance/route blood travels in order to reach ROI₁.

In one example, an offset used between when a fiducial point (e.g., asystolic peak) occurs in the PPG signal 783, and when it manifests ineach of the iPPG signals may be a fixed offset (e.g., an offset that isa function of the relative location of ROI₂ from ROI₁). In anotherexample, different sub-regions of ROI₂ (e.g., corresponding to differentpixels in the images 785) may have different offsets that are calculatedempirically relative to the timings of the PPG signal. In still anotherexample, the iPPG signals are extracted from the images based on valuesof time-segments in which the iPPG signals were expected to appear as afunction of the locations of respective regions of the iPPG signalsrelative to the location of the contact PPG device.

An offset used between when a fiducial point (e.g., a systolic peak)occurs in the PPG signal 783, and when it manifests in in each of theiPPG signals may be adjusted to account for blood velocity. For example,the offset may be inversely proportional to the heart rate and/or bloodpressure determined from the PPG signal 783. When the heart rate and/orblood pressure increase, this is usually correlated with a highervelocity of blood flow, which will tend to reduce the difference inmanifestations of a pulse wave in ROI₁ and ROI₂.

It is to be noted that offsets used between times of fiducial pointsidentified in the PPG signal 783 and the iPPG signals may beuser-specific and learned overtime. For example, histograms of theoffsets between the maxima in the PPG signal 783 and the maxima of eachof the iPPG signals, as observed over multiple pulses of the user, canbe aggregated. Based on these histograms, the most frequent offset canbe used to represent the difference between when systolic peaks occur inthe PPG signal 783 and when it manifests in each of the iPPG signals.

In another embodiment, times corresponding to fiducial points, asdetermined based on the PPG signal 783, may be used to set a range oftimes during which the same fiducial point is expected to manifest in aniPPG signal (from among the iPPG signals). For example, if a systolicpeak is observed at time tin the PPG signal 783, a manifestation of asystolic peak will be extracted from a time that falls in [t+a, t+b],where a<b, and the values of a and b are set to correspond to theminimum and maximum offsets between manifestations of systolic peaks inROI₁ and a sub-region of ROI₂ to which the iPPG signal corresponds. Asdiscussed above, the values a and b may also be adjusted according tovalues such as the heart rate and/or blood pressure, and may also belearned for a specific user.

In some embodiments, the computer 780 may utilize the PPG signal 783 toverify the quality of the iPPG signals. Optionally, the computer 780 mayrefrain from utilizing iPPG signals in calculations when they exhibit asignificant difference from the PPG signal 783. For example, if a heartrate calculated based on the PPG signal 783, during a certain period, issignificantly different from a heart rate calculated based on the iPPGsignals during that period (e.g., a difference greater than a thresholdof ±5 bpm), then that may indicate the iPPG signals during the certainperiod were noisy and/or unreliable.

Additionally, using the PPG signal 783, as described above, to assessvarious sub-regions of ROI₂, can serve as a quality filter to selectwhich regions of the face should be used to perform detection ofphysiological responses. If a certain region displays consistently anaccurate iPPG signal, it may be more reliable for detection of thephysiological response than a region from which an accurate signalcannot be extracted.

Another way to describe the benefit of measuring simultaneously the PPGsignal 783 and iPPG signals on the head involves the fact that often theiPPG signals are weak relative to the noise. Therefore, automaticdetection of the iPPG signals requires discrimination between true PPGpulses and random fluctuations due to the noise. In one embodiment, analgorithm for the selection of the iPPG pulses is based on the values oftime-segments in which the iPPG signals are expected to appear as afunction of their location relative to the location of the PPG device782. Optionally, the detected iPPG signals in these time-segments areidentified as iPPG signals if they meet one or more criteria based on(i) the spatial waveform of the iPPG signals relative to the referencePPG signal, (ii) correlation between each iPPG signal in the currenttime-segment and a predetermined number of neighboring time-segments,and (iii) correlations between iPPG signals extracted from neighboringregions of exposed skin on the head, which are expected to showessentially the same rhythm with a bounded time delay. Optionally, thesignals are taken as iPPG signals if minimal values of the criteria areobtained in several time-segments. The minimal values and the number oftime-segments can be determined in order to achieve minimal standarddeviation of the differences between the values of the heart rateextracted from the noisy iPPG signals and the reference heart rateextracted from the less noisy PPG signal.

In some embodiments, the iPPG signals include multiple values fordifferent sub-regions of ROI₂, and the physiological response isdetected based on differences between amplitudes of the valuesrecognizable in the different sub-regions of ROI₂. For example, eachsub-region may be captured by a subset of pixels in the images 785.

In one embodiment, the physiological response is indicative of anallergic reaction, and the sub-regions of ROI₂ include portions of atleast two of the following areas on the user's face: nose, upper lip,lips, cheeks, temples, periorbital area around the eyes, and theforehead. Optionally, the computer 780 detects the allergic reactionbased on changes in blood flow which manifest in iPPG signalscorresponding to the at least two areas.

In another embodiment, the physiological response is indicative of astroke, and the sub-regions of ROI₂ include at least one of thefollowing pairs on the user's face: left and right cheeks, left andright temples, left and right sides of the forehead, and left and rightsides of the periorbital area around the eyes. Optionally, the computer780 detects the stroke based on a difference in blood flow on the twosides of the face.

In yet another embodiment, the physiological response is indicative of amigraine, and the sub-regions of ROI₂ include at least one of thefollowing pairs on the user's face: left and right sides of theforehead, left and right temples, left and right sides of theperiorbital area around the eyes, and left and right cheeks.

In still another embodiment, the physiological response is indicative ofa blood pressure value that is calculated based on differences in pulsetransit times detectable in the sub-regions of ROI₂. Optionally, thesub-regions comprise at least two of the following areas on the user'sface: left temple, right temple, left side of the forehead, right sideof the forehead, left check, right cheek, nose, periorbital area aroundthe left eye, and periorbital area around the right eye.

And in yet another embodiment, the physiological response is indicativeof at least one of stress, emotional response, and pain, which arecalculated based on changes to hemoglobin concentrations observable inthe iPPG signals relative to previous measurements of hemoglobinconcentrations observable in the iPPG signals of the user. Optionally,the sub-regions of ROI₂ include at least two of the following areas onthe user's face: lips, upper lip, chin, left temple, right temple, leftside of the forehead, right side of the forehead, left check, rightcheek, left ear lobe, right ear lobe, nose, periorbital area around theleft eye, and periorbital area around the right eye.

In one embodiment, the computer 780 is a head-mounted computer.Optionally, detecting the physiological response involves performing atleast the following: identifying times at which fiducial points appearin the PPG signal; calculating, based on the times, time-segments inwhich the fiducial points are expected to appear in imagingphotoplethysmogram signals recognizable the images (iPPG signals); anddetecting a physiological response based on values of the iPPG signalsduring the time-segments.

As part of the calculations involved in detecting the physiologicalresponse, the computer 780 may perform various filtering and/orprocessing procedures to the PPG signal 783, the images 785, and/or iPPGsignals extracted from the images 785. Some non-limiting examples of thepreprocessing include: normalization of pixel intensities (e.g., toobtain a zero-mean unit variance time series signal), and conditioning atime series signal by constructing a square wave, a sine wave, or a userdefined shape, such as that obtained from an ECG signal or a PPG signalas described in U.S. Pat. No. 8,617,081.

In some embodiments, the images 785 may undergo various preprocessing toimprove the signal, such as color space transformation (e.g.,transforming RGB images into a monochromatic color or images in adifferent color space), blind source separation using algorithms such asindependent component analysis (ICA) or principal component analysis(PCA), and various filtering techniques, such as detrending, bandpassfiltering, and/or continuous wavelet transform (CWT). Variouspreprocessing techniques known in the art that may assist in extractingiPPG signals from images are discussed in Zaunseder et al. (2018),“Cardiovascular assessment by imaging photoplethysmography—a review”,Biomedical Engineering 63(5), 617-634. An example of preprocessing thatmay be used in some embodiments is given in U.S. Pat. No. 9,020,185,titled “Systems and methods for non-contact heart rate sensing”, whichdescribes how a times-series signals obtained from video of a user canbe filtered and processed to separate an underlying pulsing signal by,for example, using an ICA algorithm.

In some embodiments, detection of the physiological response may involvecalculation of pulse arrival times (PATs) at ROI₁ and/or at one or moresub-regions of ROI₂. Optionally, a PAT calculated from an PPG signalrepresents a time at which the value representing blood volume (in thewaveform represented in the PPG) begins to rise (signaling the arrivalof the pulse). Alternatively, the PAT may be calculated as a differenttime, with respect to the pulse waveform, such as the time at which avalue representing blood volume reaches a maximum or a certainthreshold, or the PAT may be the average of the time the blood volume isabove a certain threshold. Another approach that may be utilized tocalculate a PAT from an iPPG signal is described in Sola et al.“Parametric estimation of pulse arrival time: a robust approach to pulsewave velocity”, Physiological measurement 30.7 (2009): 603, whichdescribe a family of PAT estimators based on the parametric modeling ofthe anacrotic phase of a pressure pulse.

Detection of the physiological response may involve the computerutilizing an approach that may be characterized as involving machinelearning. In some embodiments, such a detection approach may involve thecomputer generating feature values based on data that includes the PPGsignal 783, the images 785, and/or iPPG signals recognizable in theimages 785, and optionally other data. Optionally, at least some of thefeature values are based on correlations between the PPG signal 783 andthe iPPG signals. The computer 780 then utilizes a previously trainedmodel 779 to calculate one or more values indicative of whether, and/orto what extent, the user is experiencing the physiological response(which may be any one of the examples of values mentioned further aboveas being calculated by the computer 780 for this purpose).

Feature values generated based on PPG signals (e.g., the PPG signal 783and/or one or more of the iPPG signals extracted from the images 785)may include various types of values, which may be indicative of dynamicsof the blood flow at the respective regions to which the PPG signalscorrespond. Optionally, these feature values may relate to properties ofa pulse waveform, which may be a specific pulse waveform (whichcorresponds to a certain beat of the heart), or a window of pulsewaveforms (e.g., an average property of pulse waveforms in a certainwindow of time).

Some examples of feature values that may be generated based on a pulsewaveform include: the area under the pulse waveform, the amplitude ofthe pulse waveform, a derivative and/or second derivative of the pulsewaveform, a pulse waveform shape, pulse waveform energy, and pulsetransit time (to the respective ROI). Optionally, some feature valuesmay be derived from fiducial points identified in the PPG signals; thesemay include values such as magnitudes of the PPG signal at certainfiducial points, time offsets between different fiducial points, and/orother differences between fiducial points. Some examples of fiducialpoint-based feature values may include one or more of the following: amagnitude of a systolic peak, a magnitude of a diastolic peak, durationof the systolic phase, and duration of the diastolic phase. Additionalexamples of feature values may include properties of the cardiacactivity, such as the heart rate and heart rate variability (asdetermined from the PPG signal). Additionally, some feature values mayinclude values of other physiological signals that may be calculatedbased on PPG signals, such as blood pressure and cardiac output.

The aforementioned feature values may be calculated in various ways. Inone example, some feature values are calculated for each PPG signalindividually. In another example, some feature values are calculatedafter normalizing a PPG signal with respect to previous measurementsfrom the corresponding PPG device used to measure the PPG signal Inother examples, at least some of the feature values may be calculatedbased on an aggregation of multiple PPG signals (e.g., differentpixels/regions in images captured by an iPPG device), or by aggregatingvalues from multiple contact PPG devices.

In some embodiments, at least some of the feature values may includevalues indicative of correlations between the PPG signal 783 and iPPGsignals extracted from the images 785. In one example, the featurevalues may include values indicative of offsets between when certainfiducial points appear in the PPG signal 783, and when they appear ineach of the iPPG signals. In another example, the feature values mayinclude values indicative of offsets at which the correlation (e.g., ascalculated by a dot-product) between the PPG signal 783 and the iPPGsignals is maximized In still another example, the feature values mayinclude values indicative of maximal value of correlation (e.g., ascalculated by a dot-product) between the PPG signal 783 and the iPPGsignals (when using different offsets).

In some embodiments, at least some of the feature values may representcomparative values, which provide an indication of the difference inblood flow, and/or in some other property that may be derived from a PPGsignal, between various regions on the head. Optionally, such featurevalues may assist in detecting asymmetrical blood flow (and/or changesthereto).

In some embodiments, at least some of the feature values describeproperties of pulse waveforms (e.g., various types of feature valuesmentioned above), which are derived from the previous measurements ofthe user using the PPG device 782 and/or the camera 784. Optionally,these feature values may include various blood flow baselines for theuser, which correspond to a certain situation the user was in when theprevious measurements were taken.

In some embodiments, at least some of the feature values may be “raw” orminimally processed measurements of the PPG device 782 and/or the camera784. Optionally, at least some of the feature values may be pixel valuesobtained by the camera 864. Optionally, the pixel values may be providedas input to functions in order to generate the feature values that arelow-level image-based features. Some examples of low-level features,which may be derived from images, include feature generated using Gaborfilters, local binary patterns (LBP) and their derivatives, algorithmssuch as SIFT and/or SURF (and their derivatives), image keypoints,histograms of oriented gradients (HOG) descriptors, and products ofstatistical procedures such independent component analysis (ICA),principal component analysis (PCA), or linear discriminant analysis(LDA). Optionally, one or more of the feature values may be derived frommultiple images taken at different times, such as volume local binarypatterns (VLBP), cuboids, and/or optical strain-based features. In oneexample, one or more of the feature values may represent a differencebetween values of pixels at one time t and values of other pixels at adifferent region at some other time t+x (which, for example, can helpdetect different arrival times of a pulse wave).

In some embodiments, at least some feature values may be generated basedon other data sources (in addition to PPG signals). In some examples, atleast some feature values may be generated based on other sensors, suchas movement sensors (which may be head-mounted, wrist-worn, or carriedby the user some other way), head-mounted thermal cameras, or othersensors used to measure the user. In other examples, at least somefeature values may be indicative of environmental conditions, such asthe temperature, humidity, and/or extent of illumination (e.g., asobtained utilizing an outward-facing head-mounted camera). Additionally,some feature values may be indicative of physical characteristics of theuser, such as age, sex, weight, Body Mass Index (BMI), skin tone, andother characteristics and/or situations the user may be in (e.g., levelof tiredness, consumptions of various substances, etc.)

Stress is a factor that can influence the diameter of the arteries, andthus influence calculated values that relate to the PPG signals and/orblood flow. In one embodiment, the computer 780 receives a valueindicative of a stress level of the user, and generates at least one ofthe feature values based on the received value. Optionally, the valueindicative of the stress level is obtained using a thermal camera. Inone example, the system may include an inward-facing head-mountedthermal camera that takes measurements of a periorbital region of theuser, where the measurements of a periorbital region of the user areindicative of the stress level of the user. In another example, thesystem includes an inward-facing head-mounted thermal camera that takesmeasurements of a region on the forehead of the user, where themeasurements of the region on the forehead of the user are indicative ofthe stress level of the user. In still another example, the systemincludes an inward-facing head-mounted thermal camera that takesmeasurements of a region on the nose of the user, where the measurementsof the region on the nose of the user are indicative of the stress levelof the user.

Hydration is a factor that affects blood viscosity, which can affect thespeed at which the blood flows in the body. In one embodiment, thecomputer 780 receives a value indicative of a hydration level of theuser, and generates at least one of the feature values based on thereceived value. Optionally, the system includes an additional camerathat detects intensity of radiation that is reflected from a region ofexposed skin of the user, where the radiation is in spectral wavelengthschosen to be preferentially absorbed by tissue water. In one example,said wavelengths are chosen from three primary bands of wavelengths ofapproximately 1100-1350 nm, approximately 1500-1800 nm, andapproximately 2000-2300 nm. Optionally, measurements of the additionalcamera are utilized by the computer as values indicative of thehydration level of the user.

The following are examples of embodiments that utilize additional inputsto generate feature values used to detect the physiological response. Inone embodiment, the computer 780 receives a value indicative of atemperature of the user's body, and generates at least one of thefeature values based on the received value. In another embodiment, thecomputer 780 receives a value indicative of a movement of the user'sbody, and generates at least one of the feature values based on thereceived value. For example, the computer 780 may receive the input forma head-mounted Inertial Measurement Unit (IMU 778) that includes acombination of accelerometers, gyroscopes, and optionally magnetometers,and/or an IMU in a mobile device carried by the user. In yet anotherembodiment, the computer 780 receives a value indicative of anorientation of the user's head, and generates at least one of thefeature values based on the received value. For example, the computer780 may receive the values indicative of the head's orientation from anoutward-facing head-mounted camera, and/or from a nearby non-wearablevideo camera. In still another embodiment, the computer 780 receives avalue indicative of consumption of a substance by the user, andgenerates at least one of the feature values based on the receivedvalue. Optionally, the substance comprises a vasodilator and/or avasoconstrictor.

The model 779 utilized to detect the physiological response may begenerated, in some embodiments, based on data obtained from one or moreusers. In the case where the physiological response is a certain medicalcondition (e.g., an allergic reaction and/or a migraine), at least someof the data used to train the model 779 corresponds to times in whichthe one or more users were not affected by the physiological response,and additional data used to train the model was obtained while thephysiological response occurred and/or following that time. Thus, thistraining data may reflect PPG signals and/or blood flow both at normaltimes, and changes to PPG signals and/or blood flow that may ensue dueto the physiological response. In the case where the physiologicalresponse corresponds to a value of a physiological signal (e.g., bloodpressure), data used to train the model 779 may include measurements ofthe one or more users that are associated with a reference value for thephysiological signal (e.g., the reference values may be blood pressurevalues measured by an external device).

The aforementioned training data may be used to generate samples, eachsample including feature values generated based on PPG signals of acertain user, additional optional data (as described above), and alabel. The PPG signals include measurements of the certain user (e.g.,taken with the PPG device 782 and the camera 784) at a certain time, andoptionally previous measurements of the user taken before the certaintime. The label is a value related to the physiological response (e.g.,an indication of the extent of the physiological response). For example,the label may be indicative of whether the user, at the certain time,experienced a certain physiological response (e.g., an allergic reactionor a stroke). In another example, the label may be indicative of theextent or severity of the physiological response at the certain time. Inyet another example, the label may be indicative of the duration untilan onset of the physiological response. In still another example, thelabel may be indicative of the duration that has elapsed since the onsetof the physiological response.

In some embodiments, the model 779 used by the computer 780 to detectthe physiological response of a specific user may be generated, at leastin part, based on data that includes previous measurements of thespecific user (and as such, may be considered personalized to someextent for the specific user). Additionally or alternatively, in someembodiments, the model 779 may be generated based on data of otherusers. Optionally, the data used to train the model 779 may include dataobtained from a diverse set of users (e.g., users of different ages,weights, sexes, preexisting medical conditions, etc.). Optionally, thedata used to train the model 779 includes data of other users withsimilar characteristics to the specific user (e.g., similar weight, age,sex, height, and/or preexisting condition).

In order to achieve a robust model, which may be useful for detectingthe physiological response for a diverse set of conditions, in someembodiments, the samples used for the training of the model 779 mayinclude samples based on data collected when users were in differentconditions. Optionally, the samples are generated based on datacollected on different days, while indoors and outdoors, and whiledifferent environmental conditions persisted. In one example, the model779 is trained on samples generated from a first set of training datataken during daytime, and is also trained on other samples generatedfrom a second set of training data taken during nighttime. In a secondexample, the model 779 is trained on samples generated from a first setof training data taken while users were exercising and moving, and isalso trained on other samples generated from a second set of data takenwhile users were sitting and not exercising.

Utilizing the model 779 to detect the physiological response may involvethe computer 780 performing various operations, depending on the type ofmodel. The following are some examples of various possibilities for themodel 779 and the type of calculations that may be accordingly performedby the computer 780, in some embodiments, in order to calculate acertain value indicative of an extent of the physiological responseexperienced by the user: (a) the model 779 comprises parameters of adecision tree. Optionally, the computer 780 simulates a traversal alonga path in the decision tree, determining which branches to take based onthe feature values. The certain value may be obtained at the leaf nodeand/or based on calculations involving values on nodes and/or edgesalong the path; (b) the model 779 comprises parameters of a regressionmodel (e.g., regression coefficients in a linear regression model or alogistic regression model). Optionally, the computer 780 multiplies thefeature values (which may be considered a regressor) with the parametersof the regression model in order to obtain the certain value; and/or (c)the model 779 comprises parameters of a neural network. For example, theparameters may include values defining at least the following: (i) aninterconnection pattern between different layers of neurons, (ii)weights of the interconnections, and (iii) activation functions thatconvert each neuron's weighted input to its output activation.Optionally, the computer 780 provides the feature values as inputs tothe neural network, computes the values of the various activationfunctions and propagates values between layers, and obtains an outputfrom the network, which is the certain value

In some embodiments, a machine learning approach that may be applied tocalculating a value indicative of the extent of the physiologicalresponse experienced by the user may be characterized as “deeplearning”. In one embodiment, the model 779 may include parametersdescribing multiple hidden layers of a neural network. Optionally, themodel 779 may include a convolution neural network (CNN). In oneexample, the CNN may be utilized to identify certain patterns in theimages 785, such as the patterns of corresponding to blood volumeeffects and ballistocardiographic effects of the cardiac pulse. Due tothe fact that calculating the value indicative of the extent of thephysiological response may be based on multiple, possibly successive,images that display a certain pattern of change over time (i.e., acrossmultiple frames), these calculations may involve retaining stateinformation that is based on previous images. Optionally, the model 779may include parameters that describe an architecture that supports sucha capability. In one example, the model 779 may include parameters of arecurrent neural network (RNN), which is a connectionist model thatcaptures the dynamics of sequences of samples via cycles in thenetwork's nodes. This enables RNNs to retain a state that can representinformation from an arbitrarily long context window. In one example, theRNN may be implemented using a long short-term memory (LSTM)architecture. In another example, the RNN may be implemented using abidirectional recurrent neural network architecture (BRNN).

In some embodiments, the system illustrated in FIG. 2a may optionallyinclude a head-mounted sensor 788 configured to measure a signalindicative of timing of the user's inhalation phase and/or exhalationphase (respiratory-phase signal). Optionally, the computer 780 mayutilize the respiratory-phase signal to detect the physiologicalresponse. For example, the computer 780 may utilize correlations betweentimings of fiducial points of the iPPG signals and the respiratory-phasesignal. Optionally, at least some of the feature values generated by thecomputer 780 (when the detection of the physiological response involvesa machine learning-based approach) may be generated based on therespiratory-phase signal. For example, the feature values may includevalues indicating whether a certain fiducial point occurred while theuser was inhaling or exhaling. The head-mounted sensor 788 used tomeasure the respiratory-phase signal may include one or more of thefollowing sensors: (i) an inward-facing camera configured to take videoin which movements of the nostrils and/or lips are indicative of therespiratory-phase signal, (ii) an inward-facing thermal cameraconfigured to measure temperatures of the upper lip and/or the mouth,which are indicative of the respiratory-phase signal, and (iii) anacoustic sensor configured to record sound waves that are indicative ofthe respiratory-phase signal.

In addition to detecting a physiological response, the systemillustrated in FIG. 2a may be utilized to authenticate the user. In oneembodiment, the camera 784 is integrated in a non-wearable device (e.g.,a smartphone or a tablet computer), and the computer 780 mayauthenticate the identity of the user and determine that the user isusing the non-wearable device by checking whether correlations betweenthe PPG signal and the iPPG signals reaches a predetermined threshold.Optionally, the predetermined threshold indicates, above a predeterminedcertainty, that the PPG signal 783 and the images 785 (in which the iPPGsignals are recognizable) belong to the same person.

The following method for detecting physiological response may be used bysystems modeled according to FIG. 2a . The steps described below may beperformed by running a computer program having instructions forimplementing the method. Optionally, the instructions may be stored on acomputer-readable medium, which may optionally be a non-transitorycomputer-readable medium. In response to execution by a system includinga processor and memory, the instructions cause the system to perform thefollowing steps:

In Step 1, measuring a signal indicative of a PPG signal at a firstregion that includes exposed skin on a user's head (referred to as a PPGsignal) utilizing a head-mounted contact PPG device. In one example, thehead-mounted contact PPG device is the PPG device 782.

In Step 2, capturing images of a second region that includes exposedskin on the user's head utilizing a camera. Optionally, the camera islocated more than 10 mm away from the user's head. Optionally, thecamera used in this step is the camera 784.

And in Step 3, detecting a physiological response based on: (i) imagingphotoplethysmogram signals (iPPG signals) recognizable in the images,and (ii) correlations between the PPG signal and the iPPG signals.

In some embodiments, detecting the physiological response is doneutilizing a machine learning-based approach. Optionally, the methodincludes the following steps: generating feature values based on datathat includes: (i) the iPPG signals, and (ii) correlations between thePPG signal and the iPPG signals; and utilizing a model to calculate,based on the feature values, a value indicative of the extent of thephysiological response experienced by the user.

In one embodiment, the physiological response is indicative of a valueof blood pressure, and is calculated based on differences in pulsetransit times detectable in iPPG signals of sub-regions of the secondregion. Optionally, the sub-regions include at least two of thefollowing areas on the user's face: left temple, right temple, left sideof the forehead, right side of the forehead, left check, right cheek,nose, periorbital area around the left eye, and periorbital area aroundthe right eye.

In another embodiment, the physiological response is indicative of atleast one of stress, an emotional response, and pain, which arecalculated based on changes to hemoglobin concentrations observable inthe iPPG signals relative to previous measurements of hemoglobinconcentrations observable in the iPPG signals of the user. Optionally,the sub-regions comprise at least two of the following areas on theuser's face: lips, upper lip, chin, left temple, right temple, left sideof the forehead, right side of the forehead, left check, right cheek,left ear lobe, right ear lobe, nose, periorbital area around the lefteye, and periorbital area around the right eye.

In one embodiment, a low-power head-mounted iPPG system includes: (i) ahead-mounted contact PPG device configured to measure a signalindicative of a PPG signal at a first region comprising exposed skin ona user's head (PPG signal), (ii) a head-mounted camera configured tocapture images of a second region comprising exposed skin on the user'shead; wherein the camera is located more than 10 mm away from the user'shead; and (iii) a head-mounted computer configured to efficientlyextract iPPG signals from the images based on focusing the iPPGcalculations around pulse timings extracted from the PPG signal.

In one embodiment, a head-mounted iPPG system includes: (i) ahead-mounted contact PPG device configured to measure a signalindicative of PPG signal at a first region comprising exposed skin on auser's head, (ii) a head-mounted camera configured to capture images ofa second region comprising exposed skin on the user's head; wherein thecamera is located more than 10 mm away from the user's head; and (iii) ahead-mounted computer configured to: identify times at which fiducialpoints appear in the PPG signal; calculate, based on the times,time-segments in which the fiducial points are expected to appear iniPPG signals recognizable the images; and detect a physiologicalresponse based on values of the iPPG signals during the time-segments.

The following is description of additional aspects of embodiments ofsystems configured to detect physiological responses, includingembodiments for various systems that may detect physiological responsesbased on thermal measurements and/or other sources of data.

A “thermal camera” refers herein to a non-contact device that measureselectromagnetic radiation having wavelengths longer than 2500 nanometer(nm) and does not touch its region of interest (ROI). A thermal cameramay include one sensing element (pixel), or multiple sensing elementsthat are also referred to herein as “sensing pixels”, “pixels”, and/orfocal-plane array (FPA). A thermal camera may be based on an uncooledthermal sensor, such as a thermopile sensor, a microbolometer sensor(where microbolometer refers to any type of a bolometer sensor and itsequivalents), a pyroelectric sensor, or a ferroelectric sensor.

Sentences in the form of “thermal measurements of an ROI” (usuallydenoted TH_(ROI) or some variant thereof) refer to at least one of: (i)temperature measurements of the ROI (T_(ROI)), such as when usingthermopile or microbolometer sensors, and (ii) temperature changemeasurements of the ROI (ΔT_(ROI)), such as when using a pyroelectricsensor or when deriving the temperature changes from temperaturemeasurements taken at different times by a thermopile sensor or amicrobolometer sensor.

In some embodiments, a device, such as a thermal camera, may bepositioned such that it occludes an ROI on the user's face, while inother embodiments, the device may be positioned such that it does notocclude the ROI. Sentences in the form of “the system/camera does notocclude the ROI” indicate that the ROI can be observed by a third personlocated in front of the user and looking at the ROI, such as illustratedby all the ROIs in FIG. 9, FIG. 13 and FIG. 21. Sentences in the form of“the system/camera occludes the ROI” indicate that some of the ROIscannot be observed directly by that third person, such as ROIs 19 and 37that are occluded by the lenses in FIG. 3a , and ROIs 97 and 102 thatare occluded by cameras 91 and 96, respectively, in FIG. 11.

Although many of the disclosed embodiments can use occluding thermalcameras successfully, in certain scenarios, such as when using an HMS ona daily basis and/or in a normal day-to-day setting, using thermalcameras that do not occlude their ROIs on the face may provide one ormore advantages to the user, to the HMS, and/or to the thermal cameras,which may relate to one or more of the following: esthetics, betterventilation of the face, reduced weight, simplicity to wear, and reducedlikelihood to being tarnished.

A “Visible-light camera” refers to a non-contact device designed todetect at least some of the visible spectrum, such as a camera withoptical lenses and CMOS or CCD sensor.

The term “inward-facing head-mounted camera” refers to a cameraconfigured to be worn on a user's head and to remain pointed at its ROI,which is on the user's face, also when the user's head makes angular andlateral movements (such as movements with an angular velocity above 0.1rad/sec, above 0.5 rad/sec, and/or above 1 rad/sec). A head-mountedcamera (which may be inward-facing and/or outward-facing) may bephysically coupled to a frame worn on the user's head, may be attachedto eyeglass using a clip-on mechanism (configured to be attached to anddetached from the eyeglasses), or may be mounted to the user's headusing any other known device that keeps the camera in a fixed positionrelative to the user's head also when the head moves. Sentences in theform of “camera physically coupled to the frame” mean that the cameramoves with the frame, such as when the camera is fixed to (or integratedinto) the frame, or when the camera is fixed to (or integrated into) anelement that is physically coupled to the frame. The abbreviation “CAM”denotes “inward-facing head-mounted thermal camera”, the abbreviation“CAM_(out)” denotes “outward-facing head-mounted thermal camera”, theabbreviation “VCAM” denotes “inward-facing head-mounted visible-lightcamera”, and the abbreviation “VCAM_(out)” denotes “outward-facinghead-mounted visible-light camera”.

Sentences in the form of “a frame configured to be worn on a user'shead” or “a frame worn on a user's head” refer to a mechanical structurethat loads more than 50% of its weight on the user's head. For example,an eyeglasses frame may include two temples connected to two rimsconnected by a bridge; the frame in Oculus Rift™ includes the foamplaced on the user's face and the straps; and the frames in GoogleGlass™ and Spectacles by Snap Inc. are similar to eyeglasses frames.Additionally or alternatively, the frame may connect to, be affixedwithin, and/or be integrated with, a helmet (e.g., sports, motorcycle,bicycle, and/or combat helmets) and/or a brainwave-measuring headset.

When a thermal camera is inward-facing and head-mounted, challengesfaced by systems known in the art that are used to acquire thermalmeasurements, which include non-head-mounted thermal cameras, may besimplified and even eliminated with some of the embodiments describedherein. Some of these challenges may involve dealing with complicationscaused by movements of the user, image registration, ROI alignment,tracking based on hot spots or markers, and motion compensation in theIR domain.

In various embodiments, cameras are located close to a user's face, suchas at most 2 cm, 5 cm, 10 cm, 15 cm, or 20 cm from the face (herein “cm”denotes to centimeters). The distance from the face/head in sentencessuch as “a camera located less than 15 cm from the face/head” refers tothe shortest possible distance between the camera and the face/head. Thehead-mounted cameras used in various embodiments may be lightweight,such that each camera weighs below 10 g, 5 g, 1 g, and/or 0.5 g (herein“g” denotes to grams).

The following figures show various examples of HMSs equipped withhead-mounted cameras. FIG. 3a illustrates various inward-facinghead-mounted cameras coupled to an eyeglasses frame 15. Cameras 10 and12 measure regions 11 and 13 on the forehead, respectively. Cameras 18and 36 measure regions on the periorbital areas 19 and 37, respectively.The HMS further includes an optional computer 16, which may include aprocessor, memory, a battery and/or a communication module. FIG. 3billustrates a similar HMS in which inward-facing head-mounted cameras 48and 49 measure regions 41 and 41, respectively. Cameras 22 and 24measure regions 23 and 25, respectively. Camera 28 measures region 29.And cameras 26 and 43 measure regions 38 and 39, respectively.

FIG. 4 illustrates inward-facing head-mounted cameras coupled to anaugmented reality device such as Microsoft HoloLens™. FIG. 5 illustrateshead-mounted cameras coupled to a virtual reality device such asFacebook's Oculus Rift™. FIG. 6 is a side view illustration ofhead-mounted cameras coupled to an augmented reality device such asGoogle Glass™. FIG. 7 is another side view illustration of head-mountedcameras coupled to a sunglasses frame.

FIG. 8 to FIG. 11 illustrate HMSs configured to measure various ROIsrelevant to some of the embodiments describes herein. FIG. 8 illustratesa frame 35 that mounts inward-facing head-mounted cameras 30 and 31 thatmeasure regions 32 and 33 on the forehead, respectively. FIG. 9illustrates a frame 75 that mounts inward-facing head-mounted cameras 70and 71 that measure regions 72 and 73 on the forehead, respectively, andinward-facing head-mounted cameras 76 and 77 that measure regions 78 and79 on the upper lip, respectively. FIG. 10 illustrates a frame 84 thatmounts inward-facing head-mounted cameras 80 and 81 that measure regions82 and 83 on the sides of the nose, respectively. And FIG. 11illustrates a frame 90 that includes (i) inward-facing head-mountedcameras 91 and 92 that are mounted to protruding arms and measureregions 97 and 98 on the forehead, respectively, (ii) inward-facinghead-mounted cameras 95 and 96, which are also mounted to protrudingarms, which measure regions 101 and 102 on the lower part of the face,respectively, and (iii) head-mounted cameras 93 and 94 that measureregions on the periorbital areas 99 and 100, respectively.

FIG. 12 to FIG. 15 illustrate various inward-facing head-mounted camerashaving multi-pixel sensors (FPA sensors), configured to measure variousROIs relevant to some of the embodiments describes herein. FIG. 12illustrates head-mounted cameras 120 and 122 that measure regions 121and 123 on the forehead, respectively, and mounts head-mounted camera124 that measure region 125 on the nose. FIG. 13 illustrateshead-mounted cameras 126 and 128 that measure regions 127 and 129 on theupper lip, respectively, in addition to the head-mounted cameras alreadydescribed in FIG. 12. FIG. 14 illustrates head-mounted cameras 130 and132 that measure larger regions 131 and 133 on the upper lip and thesides of the nose, respectively. And FIG. 15 illustrates head-mountedcameras 134 and 137 that measure regions 135 and 138 on the right andleft cheeks and right and left sides of the mouth, respectively, inaddition to the head-mounted cameras already described in FIG. 14.

In some embodiments, the head-mounted cameras may be physically coupledto the frame using a clip-on device configured to be attached/detachedfrom a pair of eyeglasses in order to secure/release the device to/fromthe eyeglasses, multiple times. The clip-on device holds at least aninward-facing camera, a processor, a battery, and a wirelesscommunication module. Most of the clip-on device may be located in frontof the frame (as illustrated in FIG. 16b , FIG. 17b , and FIG. 20), oralternatively, most of the clip-on device may be located behind theframe, as illustrated in FIG. 18b and FIG. 19 b.

FIG. 16a , FIG. 16b , and FIG. 16c illustrate two right and left clip-ondevices 141 and 142, respectively, configured to attached/detached froman eyeglasses frame 140. The clip-on device 142 includes aninward-facing head-mounted camera 143 pointed at a region on the lowerpart of the face (such as the upper lip, mouth, nose, and/or cheek), aninward-facing head-mounted camera 144 pointed at the forehead, and otherelectronics 145 (such as a processor, a battery, and/or a wirelesscommunication module). The clip-on devices 141 and 142 may includeadditional cameras illustrated in the drawings as black circles.

FIG. 17a and FIG. 17b illustrate a clip-on device 147 that includes aninward-facing head-mounted camera 148 pointed at a region on the lowerpart of the face (such as the nose), and an inward-facing head-mountedcamera 149 pointed at the forehead. The other electronics (such as aprocessor, a battery, and/or a wireless communication module) is locatedinside the box 150, which also holds the cameras 148 and 149.

FIG. 18a and FIG. 18b illustrate two right and left clip-on devices 160and 161, respectively, configured to be attached behind an eyeglassesframe 165. The clip-on device 160 includes an inward-facing head-mountedcamera 162 pointed at a region on the lower part of the face (such asthe upper lip, mouth, nose, and/or cheek), an inward-facing head-mountedcamera 163 pointed at the forehead, and other electronics 164 (such as aprocessor, a battery, and/or a wireless communication module). Theclip-on devices 160 and 161 may include additional cameras illustratedin the drawings as black circles.

FIG. 19a and FIG. 19b illustrate a single-unit clip-on device 170,configured to be attached behind an eyeglasses frame 176. Thesingle-unit clip-on device 170 includes inward-facing head-mountedcameras 171 and 172 pointed at regions on the lower part of the face(such as the upper lip, mouth, nose, and/or cheek), inward-facinghead-mounted cameras 173 and 174 pointed at the forehead, a spring 175configured to apply force that holds the clip-on device 170 to the frame176, and other electronics 177 (such as a processor, a battery, and/or awireless communication module). The clip-on device 170 may includeadditional cameras illustrated in the drawings as black circles.

FIG. 20 illustrates two right and left clip-on devices 153 and 154,respectively, configured to attached/detached from an eyeglasses frame,and having protruding arms to hold the inward-facing head-mountedcameras. Head-mounted camera 155 measures a region on the lower part ofthe face, head-mounted camera 156 measures regions on the forehead, andthe left clip-on device 154 further includes other electronics 157 (suchas a processor, a battery, and/or a wireless communication module). Theclip-on devices 153 and 154 may include additional cameras illustratedin the drawings as black circles.

It is noted that the elliptic and other shapes of the ROIs in some ofthe drawings are just for illustration purposes, and the actual shapesof the ROIs are usually not as illustrated. It is possible to calculatethe accurate shape of an ROI using various methods, such as acomputerized simulation using a 3D model of the face and a model of ahead-mounted system (HMS) to which a thermal camera is physicallycoupled, or by placing a LED instead of the sensor (while maintainingthe same field of view) and observing the illumination pattern on theface. Furthermore, illustrations and discussions of a camera representone or more cameras, where each camera may have the same FOV and/ordifferent FOVs. Unless indicated to the contrary, the cameras mayinclude one or more sensing elements (pixels), even when multiplesensing elements do not explicitly appear in the figures; when a cameraincludes multiple sensing elements then the illustrated ROI usuallyrefers to the total ROI captured by the camera, which is made ofmultiple regions that are respectively captured by the different sensingelements. The positions of the cameras in the figures are just forillustration, and the cameras may be placed at other positions on theHMS.

Sentences in the form of an “ROI on an area”, such as ROI on theforehead or an ROI on the nose, refer to at least a portion of the area.Depending on the context, and especially when using a CAM having justone pixel or a small number of pixels, the ROI may cover another area(in addition to the area). For example, a sentence in the form of “anROI on the nose” may refer to either: 100% of the ROI is on the nose, orsome of the ROI is on the nose and some of the ROI is on the upper lip.

Various embodiments described herein involve detections of physiologicalresponses based on user measurements. Some examples of physiologicalresponses include stress, an allergic reaction, an asthma attack, astroke, dehydration, intoxication, or a headache (which includes amigraine). Other examples of physiological responses includemanifestations of fear, startle, sexual arousal, anxiety, joy, pain orguilt. Still other examples of physiological responses includephysiological signals such as a heart rate or a value of a respiratoryparameter of the user. Optionally, detecting a physiological responsemay involve one or more of the following: determining whether the userhas/had the physiological response, identifying an imminent attackassociated with the physiological response, and/or calculating theextent of the physiological response.

In some embodiments, detection of the physiological response is done byprocessing thermal measurements that fall within a certain window oftime that characterizes the physiological response. For example,depending on the physiological response, the window may be five secondslong, thirty seconds long, two minutes long, five minutes long, fifteenminutes long, or one hour long. Detecting the physiological response mayinvolve analysis of thermal measurements taken during multiple of theabove-described windows, such as measurements taken during differentdays. In some embodiments, a computer may receive a stream of thermalmeasurements, taken while the user wears an HMS with coupled thermalcameras during the day, and periodically evaluate measurements that fallwithin a sliding window of a certain size.

In some embodiments, models are generated based on measurements takenover long periods. Sentences of the form of “measurements taken duringdifferent days” or “measurements taken over more than a week” are notlimited to continuous measurements spanning the different days or overthe week, respectively. For example, “measurements taken over more thana week” may be taken by eyeglasses equipped with thermal cameras, whichare worn for more than a week, 8 hours a day. In this example, the useris not required to wear the eyeglasses while sleeping in order to takemeasurements over more than a week. Similarly, sentences of the form of“measurements taken over more than 5 days, at least 2 hours a day” referto a set comprising at least 10 measurements taken over 5 differentdays, where at least two measurements are taken each day at timesseparated by at least two hours.

Utilizing measurements taken of a long period (e.g., measurements takenon “different days”) may have an advantage, in some embodiments, ofcontributing to the generalizability of a trained model. Measurementstaken over the long period likely include measurements taken indifferent environments and/or measurements taken while the measured userwas in various physiological and/or mental states (e.g., before/aftermeals and/or while the measured user wassleepy/energetic/happy/depressed, etc.). Training a model on such datacan improve the performance of systems that utilize the model in thediverse settings often encountered in real-world use (as opposed tocontrolled laboratory-like settings). Additionally, taking themeasurements over the long period may have the advantage of enablingcollection of a large amount of training data that is required for somemachine learning approaches (e.g., “deep learning”).

Detecting the physiological response may involve performing varioustypes of calculations by a computer. Optionally, detecting thephysiological response may involve performing one or more of thefollowing operations: comparing thermal measurements to a threshold(when the threshold is reached that may be indicative of an occurrenceof the physiological response), comparing thermal measurements to areference time series, and/or by performing calculations that involve amodel trained using machine learning methods. Optionally, the thermalmeasurements upon which the one or more operations are performed aretaken during a window of time of a certain length, which may optionallydepend on the type of physiological response being detected. In oneexample, the window may be shorter than one or more of the followingdurations: five seconds, fifteen seconds, one minute, five minutes,thirty minute, one hour, four hours, one day, or one week. In anotherexample, the window may be longer than one or more of the aforementioneddurations. Thus, when measurements are taken over a long period, such asmeasurements taken over a period of more than a week, detection of thephysiological response at a certain time may be done based on a subsetof the measurements that falls within a certain window near the certaintime; the detection at the certain time does not necessarily involveutilizing all values collected throughout the long period.

In some embodiments, detecting the physiological response of a user mayinvolve utilizing baseline thermal measurement values, most of whichwere taken when the user was not experiencing the physiologicalresponse. Optionally, detecting the physiological response may rely onobserving a change to typical temperatures at one or more ROIs (thebaseline), where different users might have different typicaltemperatures at the ROIs (i.e., different baselines). Optionally,detecting the physiological response may rely on observing a change to abaseline level, which is determined based on previous measurements takenduring the preceding minutes and/or hours.

In some embodiments, detecting a physiological response involvesdetermining the extent of the physiological response, which may beexpressed in various ways that are indicative of the extent of thephysiological response, such as: (i) a binary value indicative ofwhether the user experienced, and/or is experiencing, the physiologicalresponse, (ii) a numerical value indicative of the magnitude of thephysiological response, (iii) a categorial value indicative of theseverity/extent of the physiological response, (iv) an expected changein thermal measurements of an ROI (denoted TH_(ROI) or some variationthereof), and/or (v) rate of change in TH_(ROI). Optionally, when thephysiological response corresponds to a physiological signal (e.g., aheart rate, a breathing rate, and an extent of frontal lobe brainactivity), the extent of the physiological response may be interpretedas the value of the physiological signal.

One approach for detecting a physiological response, which may beutilized in some embodiments, involves comparing thermal measurements ofone or more ROIs to a threshold. In these embodiments, the computer maydetect the physiological response by comparing the thermal measurements,and/or values derived therefrom (e.g., a statistic of the measurementsand/or a function of the measurements), to the threshold to determinewhether it is reached. Optionally, the threshold may include a thresholdin the time domain, a threshold in the frequency domain, an upperthreshold, and/or a lower threshold. When a threshold involves a certainchange to temperature, the certain change may be positive (increase intemperature) or negative (decrease in temperature). Differentphysiological responses described herein may involve different types ofthresholds, which may be an upper threshold (where reaching thethreshold means≥the threshold) or a lower threshold (where reaching thethreshold means≤the threshold); for example, each physiological responsemay involve at least a certain degree of heating, or at least a certaindegree cooling, at a certain ROI on the face.

Another approach for detecting a physiological response, which may beutilized in some embodiments, may be applicable when the thermalmeasurements of a user are treated as time series data. For example, thethermal measurements may include data indicative of temperatures at oneor more ROIs at different points of time during a certain period. Insome embodiments, the computer may compare thermal measurements(represented as a time series) to one or more reference time series thatcorrespond to periods of time in which the physiological responseoccurred. Additionally or alternatively, the computer may compare thethermal measurements to other reference time series corresponding totimes in which the physiological response did not occur. Optionally, ifthe similarity between the thermal measurements and a reference timeseries corresponding to a physiological response reaches a threshold,this is indicative of the fact that the thermal measurements correspondto a period of time during which the user had the physiologicalresponse. Optionally, if the similarity between the thermal measurementsand a reference time series that does not correspond to a physiologicalresponse reaches another threshold, this is indicative of the fact thatthe thermal measurements correspond to a period of time in which theuser did not have the physiological response. Time series analysis mayinvolve various forms of processing involving segmenting data, aligningdata, clustering, time warping, and various functions for determiningsimilarity between sequences of time series data. Some of the techniquesthat may be utilized in various embodiments are described in Ding, Hui,et al. “Querying and mining of time series data: experimental comparisonof representations and distance measures.” Proceedings of the VLDBEndowment 1.2 (2008): 1542-1552, and in Wang, Xiaoyue, et al.“Experimental comparison of representation methods and distance measuresfor time series data.” Data Mining and Knowledge Discovery 26.2 (2013):275-309.

Herein, “machine learning” methods refers to learning from examplesusing one or more approaches. Optionally, the approaches may beconsidered supervised, semi-supervised, and/or unsupervised methods.Examples of machine learning approaches include: decision tree learning,association rule learning, regression models, nearest neighborsclassifiers, artificial neural networks, deep learning, inductive logicprogramming, support vector machines, clustering, Bayesian networks,reinforcement learning, representation learning, similarity and metriclearning, sparse dictionary learning, genetic algorithms, rule-basedmachine learning, and/or learning classifier systems.

Herein, a “machine learning-based model” is a model trained usingmachine learning methods. For brevity's sake, at times, a “machinelearning-based model” may simply be called a “model”. Referring to amodel as being “machine learning-based” is intended to indicate that themodel is trained using machine learning methods (otherwise, “model” mayalso refer to a model generated by methods other than machine learning).

In some embodiments, which involve utilizing a machine learning-basedmodel, a computer is configured to detect the physiological response bygenerating feature values based on the thermal measurements (andpossibly other values), and/or based on values derived therefrom (e.g.,statistics of the measurements). The computer then utilizes the machinelearning-based model to calculate, based on the feature values, a valuethat is indicative of whether, and/or to what extent, the user isexperiencing (and/or is about to experience) the physiological response.Optionally, calculating said value is considered “detecting thephysiological response”. Optionally, the value calculated by thecomputer is indicative of the probability that the user has/had thephysiological response.

Herein, feature values may be considered input to a computer thatutilizes a model to perform the calculation of a value, such as thevalue indicative of the extent of the physiological response mentionedabove. It is to be noted that the terms “feature” and “feature value”may be used interchangeably when the context of their use is clear.However, a “feature” typically refers to a certain type of value, andrepresents a property, while “feature value” is the value of theproperty with a certain instance (sample). For example, a feature may betemperature at a certain ROI, while the feature value corresponding tothat feature may be 36.9° C. in one instance and 37.3° C. in anotherinstance.

In some embodiments, a machine learning-based model used to detect aphysiological response is trained based on data that includes samples.Each sample includes feature values and a label. The feature values mayinclude various types of values. At least some of the feature values ofa sample are generated based on measurements of a user taken during acertain period of time (e.g., thermal measurements taken during thecertain period of time). Optionally, some of the feature values may bebased on various other sources of information described herein. Thelabel is indicative of a physiological response of the usercorresponding to the certain period of time. Optionally, the label maybe indicative of whether the physiological response occurred during thecertain period and/or the extent of the physiological response duringthe certain period. Additionally or alternatively, the label may beindicative of how long the physiological response lasted. Labels ofsamples may be generated using various approaches, such as self-reportby users, annotation by experts that analyze the training data,automatic annotation by a computer that analyzes the training dataand/or analyzes additional data related to the training data, and/orutilizing additional sensors that provide data useful for generating thelabels. It is to be noted that herein when it is stated that a model istrained based on certain measurements (e.g., “a model trained based onTH_(ROI) taken on different days”), it means that the model was trainedon samples comprising feature values generated based on the certainmeasurements and labels corresponding to the certain measurements.Optionally, a label corresponding to a measurement is indicative of thephysiological response at the time the measurement was taken.

Various types of feature values may be generated based on thermalmeasurements. In one example, some feature values are indicative oftemperatures at certain ROIs. In another example, other feature valuesmay represent a temperature change at certain ROIs. The temperaturechanges may be with respect to a certain time and/or with respect to adifferent ROI. In order to better detect physiological responses thattake some time to manifest, in some embodiments, some feature values maydescribe temperatures (or temperature changes) at a certain ROI atdifferent points of time. Optionally, these feature values may includevarious functions and/or statistics of the thermal measurements such asminimum/maximum measurement values and/or average values during certainwindows of time.

It is to be noted that when it is stated that feature values aregenerated based on data comprising multiple sources, it means that foreach source, there is at least one feature value that is generated basedon that source (and possibly other data). For example, stating thatfeature values are generated from thermal measurements of first andsecond ROIs (TH_(ROI1) and TH_(ROI2), respectively) means that thefeature values may include a first feature value generated based onTH_(ROI1) and a second feature value generated based on TH_(ROI2).Optionally, a sample is considered generated based on measurements of auser (e.g., measurements comprising TH_(ROI1) and TH_(ROI2)) when itincludes feature values generated based on the measurements of the user.

In addition to feature values that are generated based on thermalmeasurements, in some embodiments, at least some feature values utilizedby a computer (e.g., to detect a physiological response or train a mode)may be generated based on additional sources of data that may affecttemperatures measured at various facial ROIs. Some examples of theadditional sources include: (i) measurements of the environment such astemperature, humidity level, noise level, elevation, air quality, a windspeed, precipitation, and infrared radiation; (ii) contextualinformation such as the time of day (e.g., to account for effects of thecircadian rhythm), day of month (e.g., to account for effects of thelunar rhythm), day in the year (e.g., to account for seasonal effects),and/or stage in a menstrual cycle; (iii) information about the userbeing measured such as sex, age, weight, height, and/or body build.Alternatively or additionally, at least some feature values may begenerated based on physiological signals of the user obtained by sensorsthat are not thermal cameras, such as a visible-light camera, aphotoplethysmogram (PPG) sensor, an electrocardiogram (ECG) sensor, anelectroencephalography (EEG) sensor, a galvanic skin response (GSR)sensor, or a thermistor.

The machine learning-based model used to detect a physiological responsemay be trained, in some embodiments, based on data collected inthy-to-thy, real world scenarios. As such, the data may be collected atdifferent times of the day, while users perform various activities, andin various environmental conditions. Utilizing such diverse trainingdata may enable a trained model to be more resilient to the variouseffects different conditions can have on the values of thermalmeasurements, and consequently, be able to achieve better detection ofthe physiological response in real world day-to-day scenarios.

Since real world day-to-day conditions are not the same all the time,sometimes detection of the physiological response may be hampered bywhat is referred to herein as “confounding factors”. A confoundingfactor can be a cause of warming and/or cooling of certain regions ofthe face, which is unrelated to a physiological response being detected,and as such, may reduce the accuracy of the detection of thephysiological response. Some examples of confounding factors include:(i) environmental phenomena such as direct sunlight, air conditioning,and/or wind; (ii) things that are on the user's face, which are nottypically there and/or do not characterize the faces of most users(e.g., cosmetics, ointments, sweat, hair, facial hair, skin blemishes,acne, inflammation, piercings, body paint, and food leftovers); (iii)physical activity that may affect the user's heart rate, bloodcirculation, and/or blood distribution (e.g., walking, running, jumping,and/or bending over); (iv) consumption of substances to which the bodyhas a physiological response that may involve changes to temperatures atvarious facial ROIs, such as various medications, alcohol, caffeine,tobacco, and/or certain types of food; and/or (v) disruptive facialmovements (e.g., frowning, talking, eating, drinking, sneezing, andcoughing).

Occurrences of confounding factors may not always be easily identifiedin thermal measurements. Thus, in some embodiments, systems mayincorporate measures designed to accommodate for the confoundingfactors. In some embodiments, these measures may involve generatingfeature values that are based on additional sensors, other than thethermal cameras. In some embodiments, these measures may involverefraining from detecting the physiological response, which should beinterpreted as refraining from providing an indication that the user hasthe physiological response. For example, if an occurrence of a certainconfounding factor is identified, such as strong directional sunlightthat heats one side of the face, the system may refrain from detectingthat the user had a stroke. In this example, the user may not be alertedeven though a temperature difference between symmetric ROIs on bothsides of the face reaches a threshold that, under other circumstances,would warrant alerting the user.

Training data used to train a model for detecting a physiologicalresponse may include, in some embodiments, a diverse set of samplescorresponding to various conditions, some of which involve occurrence ofconfounding factors (when there is no physiological response and/or whenthere is a physiological response). Having samples in which aconfounding factor occurs (e.g., the user is in direct sunlight ortouches the face) can lead to a model that is less susceptible towrongfully detect the physiological response (which may be considered anoccurrence of a false positive) in real world situations.

When a model is trained with training data comprising samples generatedfrom measurements of multiple users, the model may be considered ageneral model. When a model is trained with training data comprising atleast a certain proportion of samples generated from measurements of acertain user, and/or when the samples generated from the measurements ofthe certain user are associated with at least a certain proportion ofweight in the training data, the model may be considered a personalizedmodel for the certain user. Optionally, the personalized model for thecertain user provides better results for the certain user, compared to ageneral model that was not personalized for the certain user.Optionally, personalized model may be trained based on measurements ofthe certain user, which were taken while the certain user was indifferent situations; for example, train the model based on measurementstaken while the certain user had a headache/epilepsy/stress/angerattack, and while the certain user did not have said attack.Additionally or alternatively, the personalized model may be trainedbased on measurements of the certain user, which were taken over aduration long enough to span different situations; examples of such longenough durations may include: a week, a month, six months, a year, andthree years.

Training a model that is personalized for a certain user may requirecollecting a sufficient number of training samples that are generatedbased on measurements of the certain user. Thus, initially detecting thephysiological response with the certain user may be done utilizing ageneral model, which may be replaced by a personalized model for thecertain user, as a sufficiently large number of samples are generatedbased on measurements of the certain user. Another approach involvesgradually modifying a general model based on samples of the certain userin order to obtain the personalized model.

After a model is trained, the model may be provided for use by a systemthat detects the physiological response. Providing the model may involveperforming different operations. In one embodiment, providing the modelto the system involves forwarding the model to the system via a computernetwork and/or a shared computer storage medium (e.g., writing the modelto a memory that may be accessed by the system that detects thephysiological response). In another embodiment, providing the model tothe system involves storing the model in a location from which thesystem can retrieve the model, such as a database and/or cloud-basedstorage from which the system may retrieve the model. In still anotherembodiment, providing the model involves notifying the system regardingthe existence of the model and/or regarding an update to the model.Optionally, this notification includes information needed in order forthe system to obtain the model.

A model for detecting a physiological response may include differenttypes of parameters. Following are some examples of variouspossibilities for the model and the type of calculations that may beaccordingly performed by a computer in order to detect the physiologicalresponse: (a) the model comprises parameters of a decision tree.Optionally, the computer simulates a traversal along a path in thedecision tree, determining which branches to take based on the featurevalues. A value indicative of the physiological response may be obtainedat the leaf node and/or based on calculations involving values on nodesand/or edges along the path; (b) the model comprises parameters of aregression model (e.g., regression coefficients in a linear regressionmodel or a logistic regression model). Optionally, the computermultiplies the feature values (which may be considered a regressor) withthe parameters of the regression model in order to obtain the valueindicative of the physiological response; and/or (c) the model comprisesparameters of a neural network. For example, the parameters may includevalues defining at least the following: (i) an interconnection patternbetween different layers of neurons, (ii) weights of theinterconnections, and (iii) activation functions that convert eachneuron's weighted input to its output activation. Optionally, thecomputer provides the feature values as inputs to the neural network,computes the values of the various activation functions and propagatesvalues between layers, and obtains an output from the network, which isthe value indicative of the physiological response.

A user interface (UI) may be utilized, in some embodiments, to notifythe user and/or some other entity, such as a caregiver, about thephysiological response and/or present an alert responsive to anindication that the extent of the physiological response reaches athreshold. The UI may include a screen to display the notificationand/or alert, a speaker to play an audio notification, a tactile UI,and/or a vibrating UI. In some embodiments, “alerting” about aphysiological response of a user refers to informing about one or moreof the following: the occurrence of a physiological response that theuser does not usually have (e.g., a stroke, intoxication, and/ordehydration), an imminent physiological response (e.g., an allergicreaction, an epilepsy attack, and/or a migraine), and an extent of thephysiological response reaching a threshold (e.g., stress and/or angerreaching a predetermined level).

FIG. 21 illustrates a scenario in which an alert regarding a possiblestroke is issued. The figure illustrates a user wearing a frame with atleast two CAMs (562 and 563) for measuring ROIs on the right and leftcheeks (ROIs 560 and 561, respectively). The measurements indicate thatthe left side of the face is colder than the right side of the face.Based on these measurements, and possibly additional data, the systemdetects the stroke and issues an alert. Optionally, the user's facialexpression is slightly distorted and asymmetric, and a VCAM providesadditional data in the form of images that may help detecting thestroke.

The CAMs can take respiratory-related thermal measurements when theirROIs are on the user's upper lip, the user's mouth, the space where theexhale stream form the user's nose flows, and/or the space where theexhale stream from the user's mouth flows. In some embodiments, one ormore of the following respiratory parameters may be calculated based onthe respiratory-related thermal measurements taken during a certainperiod of time:

“Breathing rate” represents the number of breaths per minute the usertook during the certain period. The breathing rate may also beformulated as the average time between successive inhales and/or theaverage between successive exhales.

“Respiration volume” represents the volume of air breathed over acertain duration (usually per minute), the volume of air breathed duringa certain breath, tidal volume, and/or the ratio between two or morebreaths. For example, the respiration volume may indicate that a firstbreath was deeper than a second breath, or that breaths during a firstminute were shallower than breaths during a second minute.

“Mouth breathing vs nasal breathing” indicates whether during thecertain period the user breathed mainly through the mouth (a statecharacterized as “mouth breathing”) or mainly through the nose (a statecharacterized as “nose breathing” or “nasal breathing”). Optionally,this parameter may represent the ratio between nasal and mouthbreathing, such as a proportion of the certain period during which thebreathing was more mouth breathing, and/or the relative volume of airexhaled through the nose vs the mouth. In one example, breathing mainlythrough the mouth refers to inhaling more than 50% of the air throughthe mouth (and less than 50% of the air through the nose).

“Exhale duration/Inhale duration” represents the exhale(s) durationduring the certain period, the inhale(s) duration during the certainperiod, and/or a ratio of the two aforementioned durations. Optionally,this respiratory parameter may represent one or more of the following:(i) the average duration of the exhales and/or inhales, (ii) a maximumand/or minimum duration of the exhales and/or inhales during the certainperiod, and (iii) a proportion of times in which the duration ofexhaling and/or inhaling reached a certain threshold.

“Post-exhale breathing pause” represents the time that elapses betweenwhen the user finishes exhaling and starts inhaling again. “Post-inhalebreathing pause” represents the time that elapses between when the userfinishes inhaling and when the user starts exhaling after that. The postexhale/inhale breathing pauses may be formulated utilizing variousstatistics, such as an average post exhale/inhale breathing pause duringa certain period, a maximum or minimum duration of post exhale/inhalebreathing pause during the certain period, and/or a proportion of timesin which the duration of post exhale/inhale breathing pause reached acertain threshold.

“Dominant nostril” is the nostril through which most of the air isexhaled (when exhaling through the nose). Normally the dominant nostrilchanges during the day, and the exhale is considered balanced when theamount of air exhaled through each nostril is similar. Optionally, thebreathing may be considered balanced when the difference between thevolumes of air exhaled through the right and left nostrils is below apredetermined threshold, such as 20% or 10%. Additionally oralternatively, the breathing may be considered balanced during a certainduration around the middle of the switching from right to left or leftto right nostril dominance. For example, the certain duration ofbalanced breathing may be about 4 minutes at the middle of the switchingbetween dominant nostrils.

“Temperature of the exhale stream” may be measured based on thermalmeasurements of the stream that flows from one or both nostrils, and/orthe heat pattern generated on the upper lip by the exhale stream fromthe nose. In one example, it is not necessary to measure the exacttemperature of the exhale stream as long as the system is able todifferentiate between different temperatures of the exhale stream basedon the differences between series of thermal measurements taken atdifferent times. Optionally, the series of thermal measurements that arecompared are temperature measurements received from the same pixel(s) ofa head-mounted thermal camera.

“Shape of the exhale stream” (also referred to as “SHAPE”) representsthe three-dimensional (3D) shape of the exhale stream from at least oneof the nostrils. The SHAPE changes during the day and may reflect themental, physiological, and/or energetic state of a user. Usually thetemperature of the exhale stream is different from the temperature ofthe air in the environment; this enables a thermal camera, whichcaptures a portion of the volume through which the exhale stream flows,to take a measurement indicative of the SHAPE, and/or to differentiatebetween different shapes of the exhale stream (SHAPEs). Additionally,the temperature of the exhale stream is usually different from thetemperature of the upper lip, and thus exhale streams having differentshapes may generate different thermal patterns on the upper lip.Measuring these different thermal patterns on the upper lip may enable acomputer to differentiate between different SHAPEs. In one embodiment,differences between values measured by adjacent thermal pixels of CAM,which measure the exhale stream and/or the upper lip over different timeintervals, may correspond to different SHAPEs. In one example, it is notnecessary to measure the exact SHAPE as long as it is possible todifferentiate between different SHAPEs based on the differences betweenthe values of the adjacent thermal pixels. In another embodiment,differences between average values, measured by the same thermal pixelover different time intervals, may correspond to different SHAPEs. Instill another embodiment, the air that is within certain boundaries of a3D shape that protrudes from the user's nose, which is warmer than theenvironment air, as measured by CAM, is considered to belong to theexhale stream.

In one embodiment, the SHAPE may be represented by one or more thermalimages taken by one or more CAMs. In this embodiment, the shape maycorrespond to a certain pattern in the one or more images and/or a timeseries describing a changing pattern in multiple images. In anotherembodiment, the SHAPE may be represented by at least one of thefollowing parameters: the angle from which the exhale stream blows froma nostril, the width of the exhale stream, the length of the exhalestream, and other parameters that are indicative of the 3D SHAPE.Optionally, the SHAPE may be defined by the shape of a geometric bodythat confines it, such as a cone or a cylinder, protruding from theuser's nose. For example, the SHAPE may be represented by parameterssuch as the cone's height, the radius of the cone's base, and/or theangle between the cone's altitude axis and the nostril.

“Smoothness of the exhale stream” represents a level of smoothness ofthe exhale stream from the nose and/or the mouth. In one embodiment, thesmoothness of the exhale stream is a value that can be determined basedon observing the smoothness of a graph of the respiratory-relatedthermal measurements. Optionally, it is unnecessary for the system tomeasure the exact smoothness of the exhale stream as long as it is ableto differentiate between smoothness levels of respiratory-relatedthermal measurements taken at different times. Optionally, the comparedthermal measurements taken at different times may be measured by thesame pixels and/or by different pixels. As a rule of thumb, the smootherthe exhale stream, the lower the stress and the better the physicalcondition. For example, the exhale stream of a healthy young person isoften smoother than the exhale stream of an elderly person, who may evenexperience short pauses in the act of exhaling.

There are well known mathematical methods to calculate the smoothness ofa graph, such as Fourier transform analysis, polynomial fit,differentiability classes, multivariate differentiability classes,parametric continuity, and/or geometric continuity. In one example, thesmoothness of TH_(ROI) indicative of the exhale stream is calculatedbased on a Fourier transform of a series of TH_(ROI). In the case ofFourier transform, the smaller the power of the high-frequenciesportion, the smoother the exhale is, and vice versa. Optionally, one ormore predetermined thresholds differentiate between the high-frequencyand low-frequency portions in the frequency domain In another example,the smoothness of TH_(ROI) indicative of the exhale stream is calculatedusing a polynomial fit (with a bounded degree) of a series of TH_(ROI).Optionally, the degree of the polynomial used for the fit isproportional (e.g., linear) to the number of exhales in the time series.In the case of polynomial fit, the smoothness may be a measure of thegoodness of fit between the series of TH_(ROI) and the polynomial. Forexample, the lower the squared error, the smoother the graph isconsidered. In still another embodiment, the smoothness of TH_(ROI)indicative of the exhale stream may be calculated using a machinelearning-based model trained with training data comprising referencetime series of TH_(ROI) for which the extent of smoothness is known.

In an alternative embodiment, a microphone is used to measure the exhalesounds. The smoothness of the exhale stream may be a value that isproportional to the smoothness of the audio measurement time seriestaken by the microphone (e.g., as determined based on the power of thehigh-frequency portion obtained in a Fourier transform of the timeseries of the audio).

There are various approaches that may be employed in order to calculatevalues of one or more of the respiratory parameters mentioned abovebased on respiratory-related thermal measurements. Optionally,calculating the values of one or more of the respiratory parameters maybe based on additional inputs, such as statistics about the user (e.g.,age, gender, weight, height, and the like), indications about the user'sactivity level (e.g., input from a pedometer), and/or physiologicalsignals of the user (e.g., heart rate and respiratory rate). Roughlyspeaking, some approaches may be considered analytical approaches, whileother approaches may involve utilization of a machine learning-basedmodel.

In some embodiments, one or more of the respiratory parameters mentionedabove may be calculated based on the respiratory-related thermalmeasurements by observing differences in thermal measurements. In oneembodiment, certain pixels that have alternating temperature changes maybe identified as corresponding to exhale streams. In this embodiment,the breathing rate may be a calculated frequency of the alternatingtemperature changes at the certain pixels. In another embodiment, therelative difference in magnitude of temperature changes at differentROIs, such as the alternating temperature changes that correspond tobreathing activity, may be used to characterize different types ofbreathing. For example, if temperature changes at ROI near the nostrilsreach a first threshold, while temperature changes at an ROI related tothe mouth do not reach a second threshold, then the breathing may beconsidered nasal breathing; while if the opposite occurs, the breathingmay be considered mouth breathing. In another example, if temperaturechanges at an ROI near the left nostril and/or on the left side of theupper lip are higher than temperature changes at an ROI near the rightnostril and/or on the right side of the upper lip, then the left nostrilmay be considered the dominant nostril at the time the measurements weretaken. In still another example, the value of a respiratory parametermay be calculated as a function of one or more input values from amongthe respiratory-related thermal measurements.

In other embodiments, one or more of the respiratory parameters may becalculated by generating feature values based on the respiratory-relatedthermal measurements and utilizing a model to calculate, based on thefeature values, the value of a certain respiratory parameter from amongthe parameters mentioned above. The model for the certain respiratoryparameter is trained based on samples. Each sample comprises the featurevalues based on respiratory-related thermal measurements, taken during acertain period of time, and a label indicative of the value of thecertain respiratory parameter during the certain period of time. Forexample, the feature values generated for a sample may include thevalues of pixels measured by the one or more cameras, statistics of thevalues of the pixels, and/or functions of differences of values ofpixels at different times. Additionally or alternatively, some of thefeature values may include various low-level image analysis features,such as feature derived using Gabor filters, local binary patterns andtheir derivatives, features derived using algorithms such as SIFT, SURF,and/or ORB, image keypoints, HOG descriptors and features derived usingPCA or LDA. The labels of the samples may be obtained through variousways. Some examples of approaches for generating the labels includemanual reporting (e.g., a user notes the type of his/her breathing),manual analysis of thermal images (e.g., an expert determines a shape ofan exhale stream), and/or utilizing sensors (e.g., a chest strap thatmeasures the breathing rate and volume).

Training the model for the certain respiratory parameter based on thesamples may involve utilizing one or more machine learning-basedtraining algorithms, such as a training algorithm for a decision tree, aregression model, or a neural network. Once the model is trained, it maybe utilized to calculate the value of the certain respiratory parameterbased on feature values generated based on respiratory-related thermalmeasurements taken during a certain period, for which the label (i.e.,the value of the certain respiratory parameter) may not be known.

In one embodiment, a system configured to calculate a respiratoryparameter includes an inward-facing head-mounted thermal camera (CAM)and a computer. CAM is worn on a user's head and takes thermalmeasurements of a region below the nostrils (TH_(ROI)), where TH_(ROI)are indicative of the exhale stream. The “region below the nostrils”,which is indicative of the exhale stream, refers to one or more regionson the upper lip, the mouth, and/or air volume(s) through which theexhale streams from the nose and/or mouth flow. The flowing of thetypically warm air of the exhale stream can change the temperature atthe one or more regions, and thus thermal measurements of these one ormore regions can provide information about properties of the exhalestream. The computer (i) generates feature values based on TH_(ROI), and(ii) utilizes a model to calculate the respiratory parameter based onthe feature values. The respiratory parameter may be indicative of theuser's breathing rate, and the model may be trained based on previousTH_(ROI) of the user taken during different days. FIG. 36b illustratesone embodiment of a system for calculating a respiratory parameter. Thesystem includes a computer 445 and CAM that is coupled to the eyeglassesframe worn by the user 420 and provides TH_(ROI) 443.

The computer 445 generates feature values based on TH_(ROI) 443, andpossibly other sources of data. Then the computer utilizes a model 442to calculate, based on the feature values, a value 447 of therespiratory parameter. The value 447 may be indicative of at least oneof the following: breathing rate, respiration volume, whether the useris breathing mainly through the mouth or through the nose, exhale(inhale) duration, post-exhale (post-inhale) breathing pause, a dominantnostril, a shape of the exhale stream, smoothness of the exhale stream,and/or temperature of the exhale stream. Optionally, the respiratoryparameters calculated by the computer 445 may be indicative of therespiration volume. Optionally, the value 447 is stored (e.g., forlife-logging purposes) and/or forwarded to a software agent operating onbehalf of the user (e.g., in order for the software agent to make adecision regarding the user).

The feature values generated by the computer 445 may include any of thefeature values described in this disclosure that are utilized to detecta physiological response. Optionally, the thermal measurements mayundergo various forms of filtering and/or normalization. For example,the feature values generated based on TH_(ROI) may include: time seriesdata comprising values measured by CAM, average values of certain pixelsof CAM, and/or values measured at certain times by the certain pixels.Additionally, the feature values may include values generated based onadditional measurements of the user taken by one or more additionalsensors (e.g., measurements of heart rate, heart rate variability,brainwave activity, galvanic skin response, muscle activity, and/or anextent of movement). Additionally or alternatively, at least some of thefeature values may include measurements of the environment in which theuser is in, and/or confounding factors that may interfere with thedetection.

A user interface (UI) 448 may be utilized to present the value 447 ofthe respiratory parameter and/or present an alert (e.g., to the user 420and/or to a caregiver). In one example, UI 448 may be used to alertresponsive to an indication that the value 447 reaches a threshold(e.g., when the breathing rate exceeds a certain value and/or after theuser 420 spent a certain duration mouth breathing instead of nasalbreathing). In another example, UI 448 may be used to alert responsiveto detecting that the probability of a respiratory-related attackreaches a threshold.

In one embodiment, the value 447 may be indicative of the smoothness ofthe exhale stream. Optionally, the value 447 may be presented to theuser 420 to increase the user's awareness to the smoothness of his/herexhale stream. Optionally, responsive to detecting that the smoothnessis below a predetermined threshold, the computer 445 may issue an alertfor the user 420 (e.g., via the UI 448) in order to increase the user'sawareness to the user's breathing.

The model 442 is trained on data that includes previous TH_(ROI) of theuser 420 and possibly other users. Optionally, the previous measurementswere taken on different days and/or over a period longer than a week.Training the model 442 typically involves generating samples based onthe previous TH_(ROI) and corresponding labels indicative of values ofthe respiratory parameter. The labels may come from different sources.In one embodiment, one or more of the labels may be generated using asensor that is not a thermal camera, which may or may not be physicallycoupled to a frame worn by the user. The sensor's measurements may beanalyzed by a human expert and/or a software program in order togenerate the labels. In one example, the sensor is part of a smart shirtand/or chest strap that measures various respiratory (and other)parameters, such as Hexoskin™ smart shirt. In another embodiment, one ormore of the labels may come from an external source such as an entitythat observes the user, which may be a human observer or a softwareprogram. In yet another embodiment, one or more of the labels may beprovided by the user, for example by indicating whether he/she isbreathing through the mouth or nose and/or which nostril is dominant.

The samples used to train the model 442 usually include samplescorresponding to different values of the respiratory parameter. In someembodiments, the samples used to train the model 442 include samplesgenerated based on TH_(ROI) taken at different times of the day, whilebeing at different locations, and/or while conducting differentactivities. In one example, the samples are generated based on TH_(ROI)taken in the morning and TH_(ROI) taken in the evening. In anotherexample, the samples are generated based on TH_(ROI) of a user takenwhile being indoors, and TH_(ROI) of the user taken while beingoutdoors. In yet another example, the samples are generated based onTH_(ROI) taken while a user was sitting down, and TH_(ROI) taken whilethe user was walking, running, and/or engaging in physical exercise(e.g., dancing, biking, etc.).

Additionally or alternatively, the samples used to train the model 442may be generated based on TH_(ROI) taken while various environmentalconditions persisted. For example, the samples include first and secondsamples generated based on TH_(ROI) taken while the environment hadfirst and second temperatures, with the first temperature being at least10° C. warmer than the second temperature. In another example, thesamples include samples generated based on measurements taken whilethere were different extents of direct sunlight and/or different extentsof wind blowing.

Various computational approaches may be utilized to train the model 442based on the samples described above. In one example, training the model442 may involve selecting a threshold based on the samples. Optionally,if a certain feature value reaches the threshold then a certainrespiratory condition is detected (e.g., unsmooth breathing).Optionally, the model 442 includes a value describing the threshold. Inanother example, a machine learning-based training algorithm may beutilized to train the model 442 based on the samples. Optionally, themodel 442 includes parameters of at least one of the following types ofmodels: a regression model, a neural network, a nearest neighbor model,a support vector machine, a support vector machine for regression, anaïve Bayes model, a Bayes network, and a decision tree.

In some embodiments, a deep learning algorithm may be used to train themodel 442. In one example, the model 442 may include parametersdescribing multiple hidden layers of a neural network. In oneembodiment, when TH_(ROI) include measurements of multiple pixels, themodel 442 may include a convolution neural network (CNN). In oneexample, the CNN may be utilized to identify certain patterns in thethermal images, such as patterns of temperatures in the region of theexhale stream that may be indicative of a respiratory parameter, whichinvolve aspects such as the location, direction, size, and/or shape ofan exhale stream from the nose and/or mouth. In another example,calculating a value of a respiratory parameter, such as the breathingrate, may be done based on multiple, possibly successive, thermalmeasurements. Optionally, calculating values of the respiratoryparameter based on thermal measurements may involve retaining stateinformation that is based on previous measurements. Optionally, themodel 442 may include parameters that describe an architecture thatsupports such a capability. In one example, the model 442 may includeparameters of a recurrent neural network (RNN), which is a connectionistmodel that captures the dynamics of sequences of samples via cycles inthe network's nodes. This enables RNNs to retain a state that canrepresent information from an arbitrarily long context window. In oneexample, the RNN may be implemented using a long short-term memory(LSTM) architecture. In another example, the RNN may be implementedusing bidirectional recurrent neural network architecture (BRNN).

The computer 445 may detect a respiratory-related attack (such as anasthma attack, an epileptic attack, an anxiety attack, a panic attack,and a tantrum) based on feature values generated based on TH_(ROI) 443.The computer 445 may further receive additional inputs (such asindications of consuming a substance, a situation of the user, and/orthermal measurements of the forehead), and detect therespiratory-related attack based on the additional inputs. For example,the computer 445 may generate one or more of the feature values used tocalculate the value 447 based on the additional inputs.

In a first embodiment, the computer 445 utilizes an indication ofconsumption of a substance to detect a respiratory-related attack.Optionally, the model 442 is trained based on: a first set of TH_(ROI)taken while the user experienced a respiratory-related attack afterconsuming the substance, and a second set of TH_(ROI) taken while theuser did not experience a respiratory-related attack after consuming thesubstance. The duration to which “after consuming” refers depends on thesubstance and may last from minutes to hours. Optionally, the consumingof the substance involves consuming a certain drug and/or consuming acertain food item, and the indication is indicative of the time and/orthe amount consumed.

In a second embodiment, the computer 445 utilizes an indication of asituation of the user to detect a respiratory-related attack.Optionally, the model 442 is trained based on: a first set of TH_(ROI)taken while the user was in the situation and experienced arespiratory-related attack, and a second set of TH_(ROI) taken while theuser was in the situation and did not experience a respiratory-relatedattack. Optionally, the situation involves (i) interacting with acertain person, (ii) a type of activity the user is conducting, selectedfrom at least two different types of activities associated withdifferent levels of stress, and/or (iii) a type of activity the user isabout to conduct (e.g., within thirty minutes), selected from at leasttwo different types of activities associated with different levels ofstress.

In a third embodiment, the system includes another CAM that takesthermal measurements of a region on the forehead (TH_(F)) of the user,and the computer 445 detects a respiratory related attack based onTH_(ROI) and TH_(F). For example, TH_(ROI) and TH_(F) may be utilized togenerate one or more of the feature values used to calculate the valueindicative of the probability that the user is experiencing, or is aboutto experience, the respiratory-related attack. Optionally, the model 442was trained based on a first set of TH_(ROI) and TH_(F) taken while theuser experienced a respiratory-related attack, and a second set ofTH_(ROI) and TH_(F) taken while the user did not experience arespiratory-related attack.

The system may optionally include a sensor 435 that takes measurementsm_(move) 450 that are indicative of movements of the user 420; thesystem further detects the physiological response based on m_(move) 450.The sensor 435 may include one or more of the following sensors: agyroscope and/or an accelerometer, an outward-facing visible-lightcamera (that feeds an image processing algorithm to detect movement froma series of images), a miniature radar (such as low-power radaroperating in the range between 30 GHz and 3,000 GHz), a miniature activeelectro-optics distance measurement device (such as a miniature Lidar),and/or a triangulation wireless device (such as a GPS receiver).Optionally, the sensor 435 is physically coupled to the frame or belongsto a device carried by the user (e.g., a smartphone or a smartwatch).

In a first embodiment, the computer 445 may detect therespiratory-related attack if the value 447 of the respiratory parameterreaches a first threshold, while m_(move) 450 do not reach a secondthreshold. In one example, reaching the first threshold indicates a highbreathing rate, which may be considered too high for the user.Additionally, in this example, reaching the second threshold may meanthat the user is conducting arduous physical activity. Thus, if the useris breathing too fast and this is not because of physical activity, thenthe computer 445 detects this as an occurrence of a respiratory-relatedattack (e.g., an asthma attack or a panic attack).

In a second embodiment, the computer 445 may generate feature valuesbased on m_(move) 450 in addition to TH_(ROI) 443, and utilize anextended model to calculate, based on these feature values, a valueindicative of the probability that the user is experiencing, or is aboutto experience, the respiratory related attack. In one example, thefeature values used along with the extended model (which may be themodel 442 or another model) include one or more of the following: (i)values comprised in TH_(ROI) 443, (ii) values of a respiratory parameterof the user 420, which are generated based on TH_(ROI) 443 (iii) valuesgenerated based on additional measurements of the user 420 (e.g.,measurements of heart rate, heart rate variability, brainwave activity,galvanic skin response, muscle activity, and an extent of movement),(iv) measurements of the environment in which the user 420 was in whileTH_(ROI) 443 were taken, (v) indications of various occurrences whichmay be considered confounding factors (e.g., touching the face, thermalradiation directed at the face, or airflow directed at the face), and/or(vi) values indicative of movements of the user (which are based onm_(move) 450).

The extended model is trained on samples generated from prior m_(move)and TH_(ROI), and corresponding labels indicating times of having therespiratory-related attack. The labels may come from various sources,such as measurements of the user (e.g., to detect respiratory distress),observations by a human and/or software, and/or the indications may beself-reported by the user. The samples used to train the extended modelmay be generated based on measurements taken over different days, andencompass measurements taken when the user was in different situations.

Usually the exhaled air warms up the skin below the nostrils, and duringinhale the skin below the nostrils cools. This enables the system toidentify the exhale based on measuring an increase in the temperature ofthe skin below the nostrils an inhale, and identify the inhale based onmeasuring a decrease in the temperature of the skin below the nostrils.

Synchronizing a physical effort with the breathing is highly recommendedby therapists and sport instructors. For example, some elderly and/orunfit people can find it difficult to stand up and/or make otherphysical efforts because many of them do not exhale while making theeffort, and/or do not synchronize the physical effort with theirbreathing. These people can benefit from a system that reminds them toexhale while making the effort, and/or helps them synchronize thephysical effort with their breathing. As another example, in many kindsof physical activities it is highly recommended to exhale while making aphysical effort and/or exhale during certain movements (such as exhalewhile bending down in Uttanasana).

In one embodiment, the computer 445 determines based on m_(move) 450 andTH_(ROI) 443 whether the user exhaled while making a physical effortabove a predetermined threshold. Optionally, the computer receives afirst indication that the user is making or is about to make thephysical effort, commands a user interface (UI) to suggest the user toexhale while making the physical effort, and commands the UI to play apositive feedback in response to determining that the user managed toexhale while making the physical effort. Additionally, the computer mayfurther command the UI to play an explanation why the user should trynext time to exhale while making the physical effort in response todetermining that the user did not exhale while making the physicaleffort.

FIG. 26a to FIG. 27c illustrate how the system described above may helptrain an elderly user to exhale during effort. In FIG. 26a the systemidentifies that the user inhaled rather than exhaled while getting upfrom a sitting position in a chair; the system alerts the user aboutthis finding and suggests that next time the user should exhale whilegetting up. In FIG. 26b , the system identifies that the user exhaled atthe correct time and commends the user on doing so. Examples of physicalefforts include standing up, sitting down, manipulating with the handsan item that requires applying a significant force, defecating,dressing, leaning over, and/or lifting an item.

In FIG. 27a the system identifies that the user inhaled rather thanexhaled while bending down to the dishwasher, and presents a thumbs-downsignal (e.g., on the user's smartphone). In FIG. 27b the systemidentifies that the user exhaled while bending down to the dishwasher,and presents a thumbs-up signal. In FIG. 27c illustrates a smartphoneapp for counting the thumbs-up and thumbs-down signals identified duringa day. The app may show various statistics, such asthumbs-up/thumbs-down during the past week, from start training with theapp, according to locations the user is, while being with certainpeople, and/or organized according to types of exercises (such as afirst counter for yoga, a second counter for housework, and a thirdcounter for breathing during work time).

In one embodiment, the computer 445: (i) receives from a fitness app(also known as a personal trainer app) an indication that the usershould exhale while making a movement, (ii) determines, based onm_(move), when the user is making the movement, and (iii) determines,based on TH_(ROI), whether the user exhaled while making the movement.Optionally, the computer commands the UI to (i) play a positive feedbackin response to determining that the user managed to exhale while makingthe physical effort, and/or (ii) play an alert and/or an explanation whythe user should try next time to exhale while making the physical effortin response to determining that the user did not exhale while making thephysical effort. FIG. 28a illustrates a fitness app running onsmartphone 196, which instructs the user to exhale while bending down.CAM coupled to eyeglasses frame 181 measures the user breathing and isutilized by the fitness app that helps the user to exhale correctly.FIG. 28b illustrates instructing the user to inhale while straighteningup.

In another embodiment, the computer 445: (i) receives from a fitness appa certain number of breath cycles during which the user should perform aphysical exercise, such as keeping a static yoga pose for a certainnumber of breath cycles, or riding a spin bike at a certain speed for acertain number of breath cycles, (ii) determines, based on m_(move),when the user performs the physical exercise, and (iii) counts, based onTH_(ROI), the number of breath cycles the user had while performing thephysical exercise. Optionally, the computer commands the UI to play aninstruction switch to another physical exercise responsive to detectingthat the user performed the physical exercise for the certain number ofbreath cycles. Additionally or alternatively, the computer commands theUI to play a feedback that refers to the number of counted breath cyclesresponsive to detecting that the user performed the physical exercisefor a number of breath cycles that is lower than the certain number ofbreath cycles. FIG. 29 illustrates a fitness app running on smartphone197, which instructs the user to stay in a triangle pose for 8 breathcycles. CAM coupled to eyeglasses frame 181 measures the breathing andis utilized by the fitness app that calculates the breath cycles andcounts the time to stay in the triangle pose according to the measuredbreath cycles.

The duration of exhaling and inhaling (denoted herein t_(exhale) andt_(inhale), respectively) can have various physiological effects. Forexample, for some users, breathing with prolonged inhales (relative tothe exhales) can increase the possibility of suffering an asthma attack.In particular, keeping the duration of exhaling longer than the durationof inhaling (i.e., t_(exhale)/t_(inhale)>1, and preferablyt_(exhale)/t_(inhale)≥2) may provide many benefits, such as having acalming effect and relieving asthma symptoms. In one embodiment, acomputer is further configured to calculate, based on TH_(ROI), theratio between exhale and inhale durations (t_(exhale)/t_(inhale)).

Many people are not aware of their breathing most of the time. Thesepeople can benefit from a system that is able to calculatet_(exhale)/t_(inhale) and provide them with feedback when it isbeneficial to increase the ratio. In one embodiment, a computer suggeststhe user, via the UI, to increase t_(exhale)/t_(inhale) when it fallsbelow a threshold. Optionally, the computer updates occasionally thecalculation of t_(exhale)/t_(inhale), and suggests to progressivelyincrease t_(exhale)/t_(inhale) at least until reaching a ratio of 1.5.Optionally, the computer stops suggesting to the user to increaset_(exhale)/t_(inhale) responsive to identifying t_(exhale)/t_(inhale)≥2.In another embodiment, the computer is configured to: (i) receive afirst indication that the user's stress level reaches a first threshold,(ii) identify, based on TH_(ROI), that the ratio between exhaling andinhaling durations (t_(exhale)/t_(inhale)) is below a second thresholdthat is below 1.5, and (iii) command the UI to suggest to the user toprolong the exhale until t_(exhale)/t_(inhale) reaches a third thresholdthat is at least 1.5.

FIG. 24 illustrates a situation in which an alert is issued to a userwhen it is detected that the ratio t_(exhale)/t_(inhale) is too low.Another scenario in which such an alert may be issued to a user isillustrated in FIG. 32, which shows a virtual robot that the user seesvia augmented reality (AR). The robot urges the user to increase theratio between the duration of the user's exhales and inhales in order toalleviate the stress that builds up. Monitoring of respiratoryparameters, and in particular, the ratio t_(exhale)/t_(inhale) can helpa user address a variety of respiratory-related symptoms, as describedin the following examples.

Asthma attacks are related to a person's breathing. Identifying certainchanges in respiratory parameters, such as breathing rate above apredetermined threshold, can help a computer to detect an asthma attackbased on the thermal measurements. Optionally, the computer utilizes amodel, which was trained on previous measurements of the user takenwhile the user had an asthma attack, to detect the asthma attack basedon the thermal measurements. FIG. 33 illustrates an asthmatic patientwho receives an alert (e.g., via an augmented reality display) that hisbreathing rate increased to an extent that often precedes an asthmaattack. In addition to the breathing rate, the computer may base itsdetermination that an asthma attack is imminent on additional factors,such as sounds and/or movement analysis as described below.

In a first embodiment, the computer may receive recordings of the userobtained with a microphone. Such recordings may include sounds that canindicate that an asthma attack is imminent; these sounds may include:asthmatic breathing sounds, asthma wheezing, and/or coughing.Optionally, the computer analyzes the recordings to identify occurrencesof one or more of the above sounds. Optionally, taking into account therecordings of the user can affect how the computer issues alertsregarding an imminent asthma attack. For example, a first alert providedto the user in response to identifying the increase in the user'sbreathing rate above the predetermined threshold without identifying atleast one of the body sounds may be less intense than a second alertprovided to the user in response to identifying both the increase in theuser's breathing rate above the predetermined threshold and at least oneof the body sounds. Optionally, in the example above, the first alertmay not be issued to the user at all.

In a second embodiment, the computer may receive measurements obtainedfrom a movement sensor worn by the user and configured to measure usermovements. Some movements that may be measured and may be related to anasthma attack include: spasms, shivering, and/or sagittal planemovements indicative of one or more of asthma wheezing, coughing, and/orchest tightness. Optionally, the computer analyzes the measurements ofthe movement sensor to identify occurrences of one or more of the abovemovements. Optionally, considering the measured movements can affect howthe computer issues alerts regarding an imminent asthma attack. Forexample, a first alert provided to the user in response to identifyingan increase in the user's breathing rate above a predeterminedthreshold, without measuring a movement related to an asthma attack, isless intense than a second alert provided to the user in response toidentifying the increase in the user's breathing rate above thepredetermined threshold while measuring a movement related to an asthmaattack.

In some embodiments, a first alert may be considered less intense than asecond alert if it is less likely to draw the user's attention. Forexample, the first alert may not involve a sound effect or involve alow-volume effect, while the second alert may involve a sound effect(which may be louder than the first's). In another example, the firstalert may involve a weaker visual cue than the second alert (or novisual cue at all). Examples of visual cues include flashing lights on adevice or images brought to the foreground on a display. In stillanother example, the first alert is not provided to the user andtherefore does not draw the user's attention (while the second alert isprovided to the user).

In one embodiment, responsive to a determination that an asthma attackis imminent, the UI suggests the user to take a precaution, such asincreasing t_(exhale)/t_(inhale), preforming various breathing exercises(e.g., exercises that involve holding the breath), and/or takingmedication (e.g., medication administered using an inhaler), in order todecrease or prevent the severity of the imminent asthma attack.Optionally, detecting the signs of an imminent asthma attack includesidentifying an increase in the breathing rate above a predeterminedthreshold.

Stress is also related to a person's breathing. In one embodiment, acomputer receives a first indication that the user's stress levelreaches a threshold and receives a second indication (i) that the ratiobetween exhaling and inhaling durations is below 1.5(t_(exhale)/t_(inhale)<1.5), and/or (ii) that the user's breathing ratereached a predetermined threshold. Then the computer may command a UI tosuggest the user to increase t_(exhale)/t_(inhale) to at least 1.5.Optionally, the computer receives the first indication from a wearabledevice, calculates t_(exhale)/t_(inhale) based on TH_(ROI) (which isindicative of the exhale stream), and commands the UI to provide theuser with an auditory and/or visual feedback indicative of the change int_(exhale)/t_(inhale) in response to the suggestion to increase theratio. Optionally, the computer may command the UI to update the userabout changes in the stress level in response to increasingt_(exhale)/t_(inhale), and may provide positive reinforcement to helpthe user to maintain the required ratio at least until a certainimprovement in the stress level is achieved.

FIG. 25 illustrates one embodiment of a system configured to collectthermal measurements related to respiration, in which four inward-facinghead-mounted thermal cameras (CAMs) are coupled to the bottom of aneyeglasses frame 181. CAMs 182 and 185 are used to take thermalmeasurements of regions on the right and left sides of the upper lip(186 and 187, respectively), and CAMs 183 and 184 are used to takethermal measurements of a region on the user's mouth 188 and/or a volumeprotruding out of the user's mouth. At least some of the ROIs mayoverlap, which is illustrated as vertical lines in the overlappingareas. Optionally, one or more of the CAMs includes a microbolometerfocal-plane array (FPA) sensor or a thermopile FPA sensor.

In one embodiment, a computer detects whether the user is breathingmainly through the mouth or through the nose based on measurements takenby CAMs 182, 183, 184 and 185. Optionally, the system helps the user toprefer breathing through the nose instead of breathing through the mouthby notifying the user when he/she is breathing through the mouth, and/orby notifying the user that the ratio between mouth breathing and nosebreathing reaches a predetermined threshold. In one embodiment, thecomputer detects whether the user is breathing mainly through the rightnostril or through the left nostril based on measurements taken by CAMs182 and 185.

The system may further include an inward-facing head-mountedvisible-light camera 189 to take images (IM) of a region on the noseand/or mouth, which are used to calculate a respiratory parameter (e.g.,detect whether the user is breathing mainly through the mouth or throughthe nose, detect the inhale duration, and/or detect the post-inhalepause duration). In one embodiment, one or more feature values may begenerated based on IM. The feature values may be generated using variousimage processing techniques and represent various low-level imageproperties. Some examples of such features may include featuresgenerated using Gabor filters, local binary patterns and theirderivatives, features generated using algorithms such as SIFT, SURF,and/or ORB, and features generated using PCA or LDA. The one or morefeature values may be utilized in the calculation of the respiratoryparameter in addition to feature values generated based on the thermalmeasurements.

In one embodiment, the inward-facing head-mounted visible-light camera189 takes images of a region on the user's mouth, and IM are indicativeof whether the mouth is open or closed. A computer utilizes a model todetect, based on IM and TH_(ROI) (such as the thermal measurements takenby at least one of CAMs 182-185), whether the user is breathing mainlythrough the mouth or through the nose. Optionally, the model was trainedbased on: a first set of TH_(ROI) taken while IM was indicative that themouth is open, and a second set of TH_(ROI) taken while IM wasindicative that the mouth is closed. Optionally, the system may help theuser to prefer breathing through the nose instead of breathing throughthe mouth by notifying the user when he/she is breathing through themouth, and/or by notifying the user that the ratio between mouthbreathing and nose breathing reaches a predetermined threshold. FIG. 30illustrates notifying the user that she breathes mainly through themouth and should switch to breathing through the nose, while having aphysical exercise such as spinning FIG. 31 illustrates an exemplary UIthat shows statistics about the dominant nostril and mouth breathingduring the day.

In one embodiment, the inward-facing head-mounted visible-light camera189 takes images of a region on the nose, and the computer identifies aninhale (and/or differentiates between an inhale and a breathing pausethat follows the inhale) based on image processing of IM to detectmovements of the nose, especially at the edges of the nostrils, whichare indicative of inhaling.

FIG. 23 illustrates another embodiment of a system configured to collectthermal measurements related to respiration, in which four CAMs arecoupled to a football helmet. CAMs 190 and 191 are used to take thermalmeasurements of regions on the right and left sides of the upper lip(appear as shaded regions on the users face), and CAMs 192 and 193 areused to take thermal measurements of a region on the user's mouth and/ora volume protruding out of the user's mouth. The illustrated CAMs arelocated outside of the exhale streams of the mouth and nostrils in orderto maintain good measurement accuracy also when using thermal sensorssuch as thermopiles.

In some embodiments, the system further includes at least one in-the-earearbud comprising a microphone to measure sounds inside the ear canal. Acomputer may identify an inhale based on analysis of the recordings fromthe earbud. Optionally, the inhale sounds measured by the earbud arestronger when the dominant nostril is the nostril closer to the ear inwhich the earbud is plugged in, compared to the inhale sounds measuredby the earbud when the other nostril is the dominant nostril.Optionally, the computer detects whether the user is breathing mainlythrough the mouth or through the nose based on the thermal measurementsand the sounds measured by the earbud. And then the system can help theuser to prefer nasal breathing over mouth breathing by alerting the userwhen he/she breathes mainly through the mouth.

In some embodiments, the dominant nostril at a given time is the nostrilthrough which most of the air is exhaled (with a closed mouth).Optionally, the dominant nostril is the nostril through which at least70% of the air is exhaled. The different types of nostril dominance areillustrated in FIG. 34a to FIG. 34c . FIG. 34a is a schematicillustration of a left dominant nostril (note the significantly largerexhale stream from the left nostril). FIG. 34b is a schematicillustration of a right dominant nostril. And FIG. 34c is a schematicillustration of a balanced nasal breathing.

FIG. 35 is a schematic illustration of one embodiment of a systemconfigured to identify the dominant nostril. The system includes atleast one CAM 750, a computer 752, and an optional UI 754. CAM 750 maybe similar to the CAMs in FIG. 25. CAM 750 takes thermal measurements offirst and second ROIs below the right and left nostrils (TH_(ROI1) andTH_(ROI2), respectively) of the user. Optionally, each CAM does notocclude any of the user's mouth and nostrils. Optionally, each CAM islocated less than 15 cm from the user's face and above the user's upperlip. Optionally, each CAM weighs below 10 g or below 2 g, and utilizesmicrobolometer or thermopile sensors. Optionally, each CAM includesmultiple sensing elements that are configured to take TH_(ROI1) and/orTH_(ROI2). In one example, each CAM includes at least 6 sensingelements, and each of TH_(ROI1) and TH_(ROI2) is based on measurementsof at least 3 sensing elements. Optionally, the system includes a frameto which CAM is physically coupled.

In one embodiment, the at least one CAM includes at least first andsecond thermal cameras (CAM1 and CAM2, respectively) that take TH_(ROI1)and TH_(ROI2), respectively, located less than 15 cm from the user'sface. CAM1 is physically coupled to the right half of the frame andcaptures the exhale stream from the right nostril better than itcaptures the exhale stream from the left nostril, and CAM2 is physicallycoupled to the left half of the frame and captures the exhale streamfrom the left nostril better than it captures the exhale stream from theright nostril.

The at least one CAM may be used to capture thermal measurements ofvarious ROIs. In one embodiment, the first region of interest (ROI₁)includes a region on the right side of the user's upper lip, and thesecond region of interest (ROI₂) includes a region on the left side ofthe user's upper lip. In another embodiment, ROI₁ includes a portion ofthe volume of the air below the right nostril where the exhale streamfrom the right nostril flows and ROI₂ includes a portion of the volumeof the air below the left nostril where the exhale stream from the leftnostril flows. In yet another embodiment, the at least one CAM may takethermal measurements of a region on the mouth and/or a volume protrudingout of the mouth (TH_(ROI3)) of the user, which is indicative of theexhale stream from the mouth, and the computer identifies the dominantnostril also based on TH_(ROI3). Optionally, the computer may utilizeTH_(ROI3) similarly to how it utilizes TH_(ROI1) and TH_(ROI2) toidentify the dominant nostril (e.g., the computer may generate featurevalues based on TH_(ROI3), as discussed below).

The computer identifies the dominant nostril based on TH_(ROI1) andTH_(ROI2) (and possibly other data such as TH_(ROI1)), which were takenduring a certain duration. Optionally, the certain duration is longerthan at least one of the following durations: a duration of one exhale,a duration of one or more breathing cycles, a half a minute, a minute,and five minutes.

In one embodiment, the computer utilizes a model to identify thedominant nostril. Optionally, the model was trained based on previousTH_(ROI1), TH_(ROI2), and indications indicative of which of thenostrils was dominant while the previous TH_(ROI1) and TH_(ROI2) weretaken. In one example, the computer generates feature values based onTH_(ROI1) and TH_(ROI2) (and optionally T_(ROI3)), and utilizes themodel to calculate, based on the feature values, a value indicative ofwhich of the nostrils is dominant.

In one embodiment, the computer identifies whether the user's breathingmay be considered balanced breathing. Optionally, breathing isconsidered balanced breathing when the streams through the right and theleft nostrils are essentially equal, such as when the extent of airexhaled through the left nostril is 40% to 60% of the total of the airexhaled through the nose. Balanced breathing of a normal healthy humanusually lasts 1-4 minutes during the time of switching between thedominant nostrils. Optionally, the computer notifies the user when theuser's breathing is balanced. Optionally, the computer suggests to theuser, via a UI, to meditate during the balanced breathing

The total time the different nostrils remain dominant may be indicativeof various medical conditions. In one embodiment, when there is asignificant imbalance of the daily total time of left nostril dominancecompared to total time of right nostril dominance, and especially ifthis condition continues for two or more days (and is significantlydifferent from the user's average statistics), it may be an indicationof an approaching health problem. For example, when the total time ofleft nostril dominance is greater than the total time of right nostrildominance, the approaching problem may be more mentally related thanphysically related; and when the total time of right nostril dominanceis greater than the total time of left nostril dominance, theapproaching problem may be more physically related than mentallyrelated. In another embodiment, a greater extent of left nostrildominance is related to digestion problems, inner gas, diarrhea, andmale impotence; and a greater extent of right nostril dominance may berelated to high blood pressure, acid reflux, and ulcers.

In one embodiment, the computer monitors nostril dominance over acertain period, and issues an alert when at least one of the followingoccurs: (i) a ratio between the total times of the right and leftnostril dominance during the certain period reaches a threshold (e.g.,the threshold may be below 0.3 or above 0.7) (ii) an average time toswitch from right to left nostril dominance reaches a threshold (e.g., athreshold longer than 3 hours), and (iii) an average time to switch fromleft to right nostril dominance reaches a threshold.

The following are some examples of various applications in which thecomputer may utilize information about the dominant nostril, which isidentified based on TH_(ROI1) and TH_(ROI2), in order to assist the userin various ways.

For some people, a certain dominant nostril may be associated with ahigher frequency of having certain health problems, such as an asthmaattack or a headache Making a person aware of which nostril is moreassociated with the health problem can help the user to alleviate thehealth problem by switching the dominant nostril. Two examples of waysto switch the dominant nostril include: (i) to plug the current dominantnostril and breathe through the other nostril; and (ii) to lay on theside of the current dominant nostril (i.e., lying on the left side toswitch from left to right dominant nostril, and vice versa). In oneembodiment, the computer detects that the user is having an asthmaattack, notifies the user about the current dominant nostril (which isassociated with a higher frequency of asthma attacks), and suggests toswitch the dominant nostril (to alleviate the asthma attack). In anotherembodiment, the computer detects the user has a headache, notifies theuser about the current dominant nostril (which is associated with ahigher frequency of headaches), and suggests to switch the dominantnostril.

Achieving balanced breathing may be a desired goal at some times.Biofeedback training may help extend the duration and/or increase thefrequency at which one has balanced breathing. In one embodiment, thecomputer provides, via the UI, biofeedback for the user to achievebalanced breathing by playing a feedback. The feedback may be generatedaccording to any suitable known method, such as normally playing thefeedback when the breathing becomes more balanced, and stopping,rewinding, and/or dithering the feedback when the breathing becomes lessbalanced. Examples of feedbacks that may be used include playing amovie, running a video game, and/or playing sounds.

In a similar manner, biofeedback training may help the user to achieve arequired breathing pattern, such as making a certain nostril dominant,or learning how to change the nostril from which most of the air isexhaled using thought and optionally without touching the nostrils. Inone embodiment, the computer provides, via the UI, biofeedback for theuser to achieve the required breathing pattern by playing a feedback.The feedback may be generated according to any suitable known method,such as playing a first sound when the use exhales more air from theright nostril than the left nostril, playing a second sound when the useexhales more air from the left nostril than the right nostril, andplaying a third sound when the use exhales essentially the same from theright and left nostrils.

In one embodiment, the length of the exhale stream is considered as thedistance from the nose at which the exhale stream can still be detected.For each person, there is a threshold that may change during the day andresponsive to different situations. When the length of the exhale streamis below the threshold, it may indicate that the person is calm; andwhen the length of the exhale stream is longer than the threshold, itmay indicate excitement. In general, the shorter the length of theexhale stream the less energy is invested in the breathing process andthe less stress the person experiences. An exception may be arduousphysical activity (which can increase the length of the exhale streamdue to larger volumes of air that are breathed). In one embodiment,TH_(ROI1) and TH_(ROI2) are indicative of the length of the exhalestream, and the computer calculates level of excitement of the userbased on the length of the exhale stream. Optionally, the longer thelength, the higher the excitement/stress, and vice versa. Additionally,the relationship between the length of the exhale stream and the levelof excitement may be a function of parameters such as the time in day,the dominant nostril, the user's mental state, the user's physiologicalstate, the environmental air quality, and/or the temperature of theenvironment. In one example, the at least one CAM uses multiple sensingelements to take thermal measurements of regions located at differentlengths below the nostrils. In this example, the larger the number ofthe sensing elements that detect the exhale stream, the longer thelength of the exhale stream. Optionally, the amplitude of thetemperature changes measured by the sensing elements is also used toestimate the length, shape, and/or uniformity of the exhale stream.

Ancient yoga texts teach that learning to extend the duration of thetime gaps between inhaling and exhaling, and/or between exhaling andinhaling, increases life span. In one embodiment, the computer assiststhe user to extend the duration of the time gap between inhaling andexhaling by performing at least one of the following: (i) calculatingthe average time gap between inhaling and exhaling over a predeterminedduration, and providing the calculation to the user via a user interface(UI), (ii) calculating the average time gap between inhaling andexhaling over a first predetermined duration, and reminding the user viathe UI to practice extending the duration when the average time gap isshorter than a first predetermined threshold, and (iii) calculating theaverage time gap between inhaling and exhaling over a secondpredetermined duration, and encouraging the user via the UI when theaverage time gap reaches a second predetermined threshold. It is to benoted that to stop breathing after exhaling is considered morebeneficial but also more dangerous, therefore the system may enable theuser to select different required durations for stopping the breathingafter inhaling and for stopping breathing after exhaling.

Typically, the dominant nostril switches sides throughout the day, withthe duration between each switch varying, depending on the individualand other factors. Disruption of the typical nasal switching cycle maybe indicative of physiological imbalance, emotional imbalance, and/orsickness. For example, slower switching of the dominant nostril may be,in some cases, a precursor of some diseases. In one embodiment, thecomputer learns the typical sequence of switching between dominantnostrils based on previous measurements of the user taken over more thana week, and issues an alert upon detecting an irregularity in thesequence of changes between the dominant nostrils. In one example, theirregularity involves a switching of the dominant nostril within aperiod of time that is shorter than a certain period typical for theuser, such as shorter than forty minutes. In another example, theirregularity involves a lack of switching of the dominant nostril for aperiod that is greater than a certain period typical for the user, suchas longer than three hours. In yet another example, the cycles of thedominant nostril may be described as a time series (e.g., stating foreach minute a value indicative of the dominant nostril). In thisexample, the computer may have a record of previous time series of theuser, acquired when the user was healthy, and the computer may comparethe time series to one or more of the previous time series in order todetermine whether a sufficiently similar match is found. A lack of sucha similar match may be indicative of the irregularity.

The following is a discussion of the role of nostril dominance and otherbreathing aspects in Asian philosophy. According to Asian philosophy,and specifically the Vedas, all objects are made of the Five GreatElements, also known as the Classical elements, which include earth,water, fire, air, and space. The great elements represent types ofenergy, but they are related to the physical elements they are calledafter. During left or right nostril dominance, just one element istypically dominant in the body, and this is reflected in the form of theexhale stream (during balanced breath two elements may share dominance).When dominance in breathing is not forced, each of the five greatelements in turn may become dominant and then cedes dominance to thenext one. The normal order of dominance according to one text is: air,fire, earth, water, and space. The relative ratios of duration ofdominance are: earth—5, water—4, fire—3, air—2, space—1. The dominantelement affects breathing in two ways: the length of the exhale and theshape of the exhale stream (SHAPE). The average lengths and shapes ofthe outbreath are as follows according to one yoga textbook: earth—about24 cm, straight out of the center of the nostril. Water—about 32 cmlength, coming from the bottom of the nostril in a slight downwarddirection. Fire—8 cm, coming from the top of the nostril with an upwardslant. Air—about 16 cm, coming from the external side of the nostril(left for the left nostril and right for the right nostril) with a slantoutside. Space—very light and short breath from all parts of thenostril.

In one embodiment, the computer identifies, based on TH_(ROI1) andTH_(ROI2), the dominant element out of the five elements. Optionally,the computer monitors if relative durations and order of elements'dominance is regular, i.e. according to the order and duration ratiosspecified and optionally with approximate length as prescribed, or thereis some irregularity. In one embodiment, irregularity may indicate apotential problem with the associated gland: for earth—ovaries ortestes/prostate, water—adrenal, fire—intestines, air—none, space—thyroidand para-thyroid. In another embodiment, irregularity may indicate apotential mental and/or physiological problem(s).

If an element's dominance time (as evident from breathingcharacteristics) is too long, it may be balanced (reduced) by consumingappropriate food and/or drink. For example, air dominance can be reducedby consuming heavy oily food, fire dominance can be reduced by drinkingwater or by consuming water-absorbing food like buckwheat, and earthdominance can be reduced by eating light food with a lot of fiber.

If a dominant element is too weak (i.e., judging by breathingcharacteristics compared to the yardstick for that element, or comparingthe SHAPE to a baseline SHAPE), it can be strengthened. For example, airdominance can be strengthened by active physical movement, firedominance can be strengthened by breath-of-fire (from kundalini yoga),water dominance can be strengthened by drinking, earth can bestrengthened by eating proteins and oily food, and space dominance canbe strengthened by visualizing a picture that grows and shrinks in size.

As discussed above, the shape of the exhale stream (SHAPE) from thenostrils changes over time. With the at least one CAM it is possible, insome embodiments, to obtain measurements indicative of at least some ofthe different typical SHAPEs. A non-limiting reason for the system'sability to measure the different SHAPEs is that the exhale stream has ahigher temperature than both the typical temperature of the environmentand the typical temperature of the upper lip. As a result, the particlesof the exhale stream emit at a higher power than both the environmentand the upper lip, which enables CAM to measure the SHAPE over time.

As discussed above, different SHAPEs may be characterized by different3D shape parameters (e.g., the angle from which the exhale stream blowsfrom a nostril, the width of the exhale stream, the length of the exhalestream, and other parameters that are indicative of the 3D SHAPE).Additionally, different SHAPEs may be associated with different statesof the user, such as different physiological and/or mental conditionsthe user may be in. In some embodiments, the computer calculates theSHAPE based on TH_(ROI1) and TH_(ROI2). Optionally, calculating theshape involves calculating values of one or more parameters thatcharacterize the exhale stream's shape (e.g., parameters related to the3D SHAPE). Optionally, calculating the SHAPE involves generating areference pattern for the SHAPE. For example, the reference pattern maybe a consensus image and/or heat map that is based on TH_(ROI1) andTH_(ROI2) taken over multiple breaths.

In other embodiments, the computer identifies a SHAPE based on TH_(ROI1)and TH_(ROI2). Optionally, the identified SHAPE belongs to a set thatincludes at least first and second SHAPEs, between which the computerdifferentiates. Optionally, the first and second SHAPEs are indicativeof at least one of the following: two of the five great elementsaccording to the Vedas, two different emotional states of the user, twodifferent moods of the user, two different energetic levels of the user,and a healthy state of the user versus an unhealthy state of the user.In one example, the first SHAPE is indicative of a powerful alertenergetic level, while the second SHAPE is indicative of a tiredenergetic level, and the computer uses this information to improvecomputerized interactions with the user.

The SHAPE may be related to the dominant nostril at the time. In oneembodiment, the first SHAPE occurs more frequently when the rightnostril is dominant, and the second SHAPE occurs more frequently whenthe left nostril is dominant In another embodiment, both the first andthe second SHAPEs occur more frequently when the right nostril isdominant.

In one example, differentiating between the first and second SHAPEsmeans that there are certain first TH_(ROI1) and TH_(ROI2) that thecomputer identifies as corresponding to the first SAHPE and not ascorresponding to the second SHAPE, and there are certain secondTH_(ROI1) and TH_(ROI2) that the computer identifies as corresponding tothe second SHAPR and as not corresponding to the first SHAPE. In anotherexample, differentiating between first and second SHAPEs means thatthere are certain third TH_(ROI1) and TH_(ROI2) that the computeridentifies as having a higher affinity to the first SHAPE compared totheir affinity to the second SHAPE, and there are certain fourthT_(ROI1) and TH_(ROI2) that the computer identifies as having a higheraffinity to the second SHAPE compared to their affinity to the firstSHAPE.

In some embodiments, the SHAPE is identified by the computer based onTH_(ROI1), TH_(ROI2), and optionally other sources of data. Since theSHAPE does not typically change between consecutive breaths, detectingthe shape of the exhale may be done based on multiple measurements ofmultiple exhales. Using such multiple measurements can increase theaccuracy of the identification of the shape. In one example, the firstand second SHAPEs are identified based on first and second sets ofTH_(ROI1) and TH_(ROI2) taken during multiple exhales over first andsecond non-overlapping respective durations, each longer than a minute.

The computer may utilize different approaches to identify the SHAPE. Inone embodiment, the computer may compare TH_(ROI1) and TH_(ROI2) to oneor more reference patterns to determine whether TH_(ROI1) and TH_(ROI2)are similar to a reference pattern from among the one or more referencepatterns. For example, if the similarity to a reference pattern reachesa threshold, the exhale stream measured with TH_(ROI1) and TH_(ROI2) maybe identified as having the shape corresponding to the shape of thereference pattern. Determining whether TH_(ROI1) and TH_(ROI2) aresimilar to a reference pattern may be done using various imagesimilarity functions, such as determining the distance between eachpixel in the reference pattern and its counterpart in TH_(ROI1) andTH_(ROI2). One way this can be done is by converting TH_(ROI1) andTH_(ROI2) into a vector of pixel temperatures, and comparing it to avector of the reference pattern (using some form of vector similaritymetric like a dot product or the L2 norm).

The one or more reference patterns may be generated in different ways.In one embodiment, the one or more reference patterns are generatedbased on previous TH_(ROI1) and TH_(ROI2) of the user taken on differentdays. Optionally, the SHAPEs were known while previous TH_(ROI1) andTH_(ROI2) of the user taken. In one example, the SHAPE is associatedwith a state of the user at the time (e.g., relaxed vs. anxious). Inanother example, the SHAPE may be determined using an external thermalcamera (which is not head-mounted). In yet another example, the SHAPE isdetermined by manual annotation. In one embodiment, the one or morereference patterns are generated based on previous TH_(ROI1) andTH_(ROI2) of one or more other users.

In some embodiments, the SHAPE may be discovered through clustering.Optionally, the computer may cluster sets of previous TH_(ROI1) andTH_(ROI2) of the user into clusters. Where sets of TH_(ROI1) andTH_(ROI2) in the same cluster are similar to each other and the exhalestreams they measured are assumed to have the same shape. Thus, each ofthe clusters may be associated with a certain SHAPE to which itcorresponds. In one example, the clusters include at least first andsecond clusters that correspond to the aforementioned first and secondSHAPEs.

The computer may utilize a machine learning-based model to identify theSHAPE. In one embodiment, the computer generates feature values based onTH_(ROI1) and TH_(ROI2), and utilizes a model to classify TH_(ROI1) andTH_(ROI2) to a class corresponding to the SHAPE. Optionally, the classcorresponds to the aforementioned first or second shapes. Optionally,the model is trained based on previous TH_(ROI1) and TH_(ROI2) of theuser taken during different days.

In one embodiment, the computer receives an indication of the user'sbreathing rate, and uses this information along with the SHAPE at thattime in order to suggest to the user to perform various activitiesand/or alert the user. Optionally, the indication of the user'sbreathing rate is calculated based on TH_(ROI1) and TH_(ROI2). In oneexample, the SHAPE is correlative with the state of the user, anddifferent states combined with different breathing rates may havedifferent meaning, which cause the computer to suggest differentactivities. The different activities may vary from differentwork/learning related activities to different physical activities todifferent treatments. In one example, the computer suggests to the user,via the UI, to perform a first activity in response to detecting thatthe breathing rate reached a threshold while identifying the firstSHAPE. However, the computer suggest to the user to perform a secondactivity, which is different from the first activity, in response todetecting that the breathing rate reached the threshold whileidentifying the second SHAPE. In another example, the computer alertsthe user, via the UI, in response to detecting that the breathing ratereached a threshold while identifying the first SHAPE, and the computerdoes not alert the user in response to detecting that the breathing ratereached the threshold while identifying the second SHAPE. In thisexample, the SHAPE may be correlated with the state of the user, anddifferent states may be associated with different normal breathingrates. When the difference between the current breathing rate and thenormal breathing rate (associated with the current SHAPE) reaches athreshold, the user may be in an abnormal state that warrants an alert.

In another embodiment, the computer configures a software agent thatprioritizes activities for the user based on the identified SHAPE, suchthat a first activity is prioritized over a second activity responsiveto identifying the first SHAPE, and the second activity is prioritizedover the first activity responsive to identifying the second SHAPE. Itis noted that the system may prioritize different activities fordifferent SHAPEs also when the measured breathing rate and respirationvolume are the same.

In still another embodiment, the computer learns a flow of typicalchanges between different SHAPEs based on previous measurements of theuser, and issues an alert upon detecting an irregularity related to aflow of changes between the SHAPEs. For example, the irregularity mayinvolve a new SHAPE, more frequent changes between SHAPEs, havingcertain SHAPEs for more or less time than usual, etc.

In yet another embodiment, the computer receives data about types offoods consumed by the user, stores the data in a memory, and findscorrelations between the SHAPEs and the types of foods. Thesecorrelations may be used to make suggestions to the user. For example,the computer may suggest the user to eat a first type of food responsiveto identifying the first SHAPE, and suggest the user to eat a secondtype of food responsive to identifying the second SHAPE. According toAyurveda medicine, it is preferred to eat according to the three doshasand the five great elements. In times when the SHAPE is indicative ofthe dominant element (out of the five great elements), the computer mayguide the user which types of food suit the identified dominant element,and/or may help the user to avoid inappropriate types of foods byidentifying the types of food the user eats (and/or is about to eat),and alert the user when the identified food is inappropriate to thecurrent dominant element (that was identified based on the SHAPE).

Data obtained from monitoring the dominant nostril can be utilized tomake suggestions of activities for the user. FIG. 36a illustrates oneembodiment of a system configured to suggest activities according to thedominant nostril. The system includes a sensor 451 for takingmeasurements 454 indicative of which of the user's nostrils is dominantat the time the measurements 454 were taken. Optionally, the sensor 451is one or more thermal cameras, such as the thermal cameras illustratedin FIG. 25, however, as discussed below, other types of sensors may beutilized to take the measurements 454. The system also includes acomputer 455 and optionally includes a UI 456.

The computer 455 predicts, based on the measurements 454, which of theuser's nostrils will be the dominant nostril at a future time.Optionally, responsive to predicting that the right nostril will bedominant at the future time, the computer 455 suggests having at thefuture time a first activity, which is more suitable for a rightdominant nostril than a second activity. Optionally, responsive topredicting that the left nostril will be dominant at the future time,the computer suggests having at the future time the second activity,which is more suitable for a left dominant nostril than the firstactivity. Optionally, the computer 455 suggests activities utilizing theUI 456. In one example, the first activity requires moreverbal-analytical skills and less spatial skills compared to the secondactivity. In another example, the first activity requires more logicand/or locomotive skills compared to the second activity, and lessempathy and/or imagination. In another example, the second activityrequires more creativity and less physical effort compared to the firstactivity.

The suggestions of activities described above may be based on thepremise that the dominant nostril is indicative of which of the user'sbrain hemispheres is more effective at performing activities that areassociated with it. It is typically assumed that the left side of theuser's brain is expected to be more effective at performing tasks whenthe right nostril is dominant (compared to when the left nostril isdominant) Conversely, the right side of the user's brain is expected tobe more effective at performing tasks when the left nostril is dominant(compared to when the right nostril is dominant). The right hemisphereis usually believed to be better at expressive and creative tasks. Someof the abilities associated with the right hemisphere includerecognizing faces, expressing emotions, music, reading emotions, color,images, intuition, and creativity. The left hemisphere is usuallybelieved to be adept to tasks that involve logic, language, andanalytical thinking. The left hemisphere is usually described as beingbetter at language, logic, critical thinking, numbers, and reasoning.Thus, certain activities, which require certain skills that areassociated with a certain hemisphere, may be more suitable to performwhen one nostril is dominant compared to when the other nostril isdominant.

Additionally or alternatively, the suggestions of activities describedabove may be based on empirical data of the performances of the userand/or performances of other users. By analyzing the user's performancesversus the dominant nostril (and optionally other parameters), and/orusing big data analysis of the measured performances of many usersversus their dominant nostril (and optionally other parameters), it ispossible to identify a first set of activities that are statisticallysignificantly more successfully achieved during right dominant nostril,a second set of activities that are statistically significantly moresuccessfully achieved during left dominant nostril, and a third set ofactivities that are statistically significantly more successfullyachieved during a balanced nasal breathing.

To predict the dominant nostril at the future time, the computer 455relies on the measurements 454, which were taken prior to a currenttime, at which the prediction is made. Optionally, the future time maybe at least five minutes after the current time, at least thirty minutesafter the current time, at least one hour after the current time, atleast three hours after the current time, or at least six hours afterthe current time.

In one embodiment, the computer 455 utilizes the measurements 454 todetermine when the dominant nostril last switched (before the currenttime), and uses this information to predict when it will switch next(possibly multiple times). Thus, the computer can extrapolate, based onthe measurements 454, a timeline until the future time, indicating whichnostril is dominant at different times until (and including) the futuretime. Optionally, information useful for determining the time line (suchas the time each nostril remains dominant) may be based on themeasurements 454 and/or previous measurements of the user taken with thesensor 451 during different days.

In another embodiment, the computer 455 predicts the dominant nostril atthe future by generating feature values and utilizing a machinelearning-based model to estimate the dominant nostril at the future time(e.g., left nostril dominance, right nostril dominance, or balancedbreathing). Optionally, the feature values comprise one or more featurevalues describing aspects of the future time such as the time to whichit corresponds (e.g., how much time ahead the future time is), thelocation the user is expected to be at the future time, and/or anactivity the user is expected to partake at the future time. Optionally,the feature values may include one or more features values correspondingto a state of the user at an earlier time that precedes the future time,such as the user's dominant nostril (e.g., as determine based on themeasurements 454), manipulation of the dominant nostril performed by theuser recently, previous measurements of the user taken after the usermanipulated the dominant nostril and/or practiced pranayama and/orlistened to brainwave entrainment, an activity the user had during theearlier time, and/or values of physiological signals of the user at theearlier time. In one embodiment, the machine learning-based model istrained based on samples that include measurements 454 taken at certainearlier times and their corresponding dominant nostrils followingcertain durations after the certain earlier times.

When a first activity is suggested for the future time (over the secondactivity), it typically means that the first activity is to be preferredover the second activity. Optionally, to suggest having the firstactivity at the future time means that the computer schedules the firstactivity at the future time and does not schedule the second activity atthe future time. Additionally or alternatively, to suggest having thefirst activity at the future time means that the computer 455 ranks thefirst activity at the future time higher than it ranks the secondactivity at the future time. Optionally, when the first activity isranked higher than the second activity it means that the first activityis given a stronger recommendation than the second activity. Forexample, a stronger recommendation may involve the first activity beingsuggested by displaying it first on a list of suggested activities. Inanother example, a stronger recommendation may involve suggesting thefirst activity with a larger image, a more prominent visual effect,and/or a more noticeable auditory signal than the one used to suggestthe second activity.

The computer 455 may utilize a determination of which nostril isdominant at the current time and/or a prediction of which nostril willbe dominant at the future in order to assist the user in performingactivities at suitable times. In a first embodiment, the computer 455assists the user to spend more time eating certain types of food whenthe right nostril is dominant Additionally or alternatively, thecomputer 455 further assists the user to spend less time eating thecertain types of food when the left nostril is dominant In one example,the computer 455 may assist the user by identifying that the user startslooking for food during left nostril dominance, and reminding the userthat eating while the left nostril is dominant is probably due toemotional reasons. In another example, the computer 455 may arrange theuser's schedule such that at least 60% of the occurrences of lunchand/or dinner are planned to a time when the right nostril is dominantOptionally, the computer 455 recommends to the user to have the mainmeal of the day while the right nostril is dominant In a secondembodiment, the computer 455 assists the user to increase the time spentat the toilet defecating while the right nostril is dominant Optionally,the computer 455 recommends to the user to spend less time at the toiletdefecating while the left nostril is dominant. For example, the computer455 may recommend to go on a bathroom break when the right nostril isdominant Optionally, the computer 455 may assist the user to decreasedefecating during times of left nostril dominance by reminding the userthat it is preferred to defecate during right nostril dominance,especially when suffering from constipation. In a third embodiment, theactivity involves creativity, such as creating art, and the computer 455assists the user to spend more time on the creative activity when theleft nostril is dominant.

It is recommended to perform some activities when the breathing throughthe nose is balanced. In one embodiment, the computer 455 identifies,based on the measurements 454, times in which the breathing through thenose is balanced, and suggests a third activity for those times.Optionally, the third activity is more suitable for balanced breathingcompared to the first and second activities. Optionally, the thirdactivity requires higher self-awareness compared to the first and secondactivities. For example, the third activity may include a spiritualpractice (such as meditating or praying), while the first and secondactivities do not include spiritual practices.

Various hardware configurations may be utilized in different embodimentsof the system configured to suggest activities according to the dominantnostril, in order to take the measurements 454 of the user.

In a first embodiment, the system includes a CAM that takes thermalmeasurements of a region below the user's nostrils (e.g., CAM 183 or CAM184). In this embodiment, identifying the dominant nostril and/orwhether the breathing is balanced may be done by the computer 455 basedon signal processing of the thermal measurements taken by CAM.

In a second embodiment, the sensor 451 includes one or more implantedsensors located around the area of the nostrils. In this embodiment,identification of the dominant nostril and/or whether the breathing isbalanced may be done based on signal processing of the measurements ofthe implanted sensors.

In a third embodiment, the sensor 451 includes right and left in-the-earearbuds comprising microphones, configured to measure sounds inside theright and left ear canals; the computer 455 identifies the dominantnostril based on analysis of the recordings from the earbuds. Forexample, the computer 455 may identify the dominant nostril based on theassumption that the inhale sounds measured by the in-the-ear earbud inthe dominant side are stronger than the inhale sounds measured by thein-the-ear earbud in the non-dominant side.

In a fourth embodiment, the system includes a frame configured to beworn on the user's head, and the sensor 451 comprises a visible-lightcamera; the visible-light camera is physically coupled to the frame, andtakes images of a region on the user's nose. For example, the computer455 may identify the dominant nostril based on analyzing the images ofthe nose by identifying movements of the nose, especially at the edgesof the nostrils.

In a fifth embodiment, the sensor 451 includes thermistors that are incontact with the nostrils and/or the upper lip in order to take themeasurements. Optionally, the dominant nostril may be identified basedon signal processing of the thermistors' measurements.

In a sixth embodiment, the sensor 451 includes anemometers locatedinside the breathing streams of the nostrils in order to take themeasurements. Optionally, the dominant nostril is identified based onsignal processing of the anemometers' measurements.

In a seventh embodiment, the sensor 451 includes a non-wearable IRcamera pointed to the area around the nostrils in order to take themeasurements. Optionally, the dominant nostril is identified based onimage processing of the measurements of the non-wearable IR camera.

The suggestions provided by the computer 455 may be done as part ofvarious programs that may benefit the user. Optionally, the computer 455provides functionality of at least one of the following programs: avirtual assistant (i.e., a software agent), a calendar managementprogram, a priority management program, a project management program, a“to do” list program, a work schedule program, and a self-learningprogram.

Some embodiments of the system may involve notification of the userabout which of the nostrils is dominant at a given time (e.g., via UI456). Optionally, the notification involves providing a user with anindication (e.g., via sound and/or an image) when the dominant nostrilchanges and/or every certain period of time (e.g., every hour).Additionally or alternatively, notifying the user about which of thenostrils is dominant may involve utilizing different themes for UI 456.In one example, a first theme for UI 456 is utilized when the rightnostril is the dominant nostril, and a second theme for UI 456 isutilized when the left nostril is the dominant nostril. Optionally, thefirst theme is more logical than the second theme (e.g., presenting dataand/or suggestions involves providing more facts and/or detailedexplanations), and the second theme is more emotional than the firsttheme (e.g., presenting data and/or suggestions includes more emotionalphrases, abstract images, social-related data, and/or less factualinformation).

In one embodiment, the computer 455 is programmed to converse with theuser according to at least first and second modes. The first mode isperceived by the user as more logical than the second mode, and thesecond mode is perceived by the user as more emotional than the firstmode. The computer 455 uses, on average, the first mode more frequentlythan the second mode when the right nostril is the dominant nostril, anduses, on average, the second mode more frequently than the first modewhen the left nostril is the dominant nostril. Examples of logicalspeech include sentences built around numbers and facts, while emotionalspeech includes sentences built around emotions and intuition.

The following is a description of steps involved in one embodiment of amethod for suggesting activities according to the dominant nostril. Thesteps described below may be used by systems modeled according to FIG.36a , and may be performed by running a computer program havinginstructions for implementing the method. Optionally, the instructionsmay be stored on a computer-readable medium, which may optionally be anon-transitory computer-readable medium. In response to execution by asystem including a processor and memory, the instructions cause thesystem to perform operations of the method.

In one embodiment, the method for alerting about stress includes atleast the following steps: In Step 1, taking, utilizing a sensor,measurements of a user, which are indicative of the user's dominantnostril. In Step 2, predicting, based on the measurements, which of theuser's nostrils will be the dominant nostril at a future time (thatoccurs after the measurements in Step 1 were taken). And In Step 3,responsive to predicting that the right nostril will be dominant at thefuture time, suggesting having at the future time a first activity,which is more suitable for a right dominant nostril than a secondactivity. Optionally, responsive to predicting that the left nostrilwill be dominant at the future time, this step involves suggestinghaving at the future time the second activity, which is more suitablefor a left dominant nostril than the first activity. Optionally, themethod further includes assisting the user to decrease eating certaintypes of food during left nostril dominance, and assisting the user toschedule the main meal of the day during right nostril dominanceOptionally, the method further includes learning the typical sequence ofswitching between dominant nostrils based on previous measurements ofthe user taken over more than a week, and alerting upon detecting anirregularity in the sequence of changes between the dominant nostrils.

In some embodiments, a system is configured to detect a physiologicalresponse based on respiratory parameters. Optionally, the physiologicalresponse is stress. Optionally, the respiratory parameters include thebreathing rate and breathing rate variability (which is discussedfurther below).

The breathing rate variability (BRV) is a value that is indicative ofthe physiological phenomenon of variations between consecutive breathes,observed during a certain period of time (e.g., a minute). In a similarfashion to heart rate variability (HRV), which is the physiologicalphenomenon of variations between consecutive heartbeats, the extent ofBRV can be indicative of various physiological phenomena, such as stressand/or physiological state.

In one embodiment, stress is detected based on thermal measurements ofROIs indicative of respiration performances, such as the mouth area, theupper lip area, and/or an air volume below the nostrils where the exhalefrom the nose flows. Optionally, TH_(ROI) may be utilized to calculatevarious respiratory parameters, which include the breathing rate and/orthe BRV.

The duration between successive breaths (such as the time betweenstarting successive exhales) and/or breathing irregularity may becalculated using various methods, such as geometric methods,frequency-domain methods, and/or non-linear methods. The computer maycalculate the BRV based on TH_(ROI) taken during different periods oftime, such as at least one minute long or at least 5 minutes long.

In one embodiment, the breathing rate variability (BRV) and thebreathing rate (BR) are utilized by a computer in order to detect whenthe user is stressed. Optionally, elevated BRV in addition to elevatedBR may serve as an indicator of stress. Optionally, elevated BRV, evenwhen the BR is reduced, may serve as an indicator of stress. Forexample, the computer may calculate BR₁ and BRV₁ based on TH_(ROI) takenduring a first period, calculate BR₂ and BRV₂ based on TH_(ROI) takenduring a second following period, and determine that the user's stresslevel is higher at the second period relative to the first periodbecause (BRV₁<BRV₂), even though (BR₁>BR₂).

In one embodiment, the computer calculates the stress level based oncomparing BR and BRV to various thresholds that correspond to differentstress levels. In one example, having a high BRV may lower the thresholdon BR that is required in order to detect stress.

In another embodiment, the computer may utilize a machine learning-basedmodel in order to detect the stress level. Optionally, the computerutilizes TH_(ROI) to generate feature values indicative of the BR and/orthe BRV, and the model was trained based on samples that each includefeature values based on TH_(ROI) and labels indicative of the user'sstress level.

Some embodiments described herein involve utilization of at least oneinward-facing head-mounted thermal cameras (such a camera is denotedbelow CAM) to take thermal measurements of a region below the nostrils(these measurements are denoted below TH_(RBN)). TH_(RBN) are indicativeof an exhale stream of the user, such as air exhaled from a nostriland/or the mouth of the user. Since exhaled air usually has a differenttemperature than the environment and/or the human skin, TH_(RBN) canprovide indications regarding the user's respiratory activity, such asthe breathing rate, whether exhaling is done through the mouth or nose,the respiration volume, and other respiratory parameters describedherein. Additionally or alternatively, TH_(RBN) may be used to calculatean aerobic activity parameter (as illustrated in FIG. 37a ) and/or acoaching indication (as illustrated in FIG. 37b ). The following is adescription of embodiments of such systems that utilize TH_(RBN) forsuch respiratory-related applications. The systems illustrated in FIG.37a and FIG. 37b include at least one CAM and a computer.

The at least one CAM may include various combinations of one or moreCAMs, as described in the various examples given in this disclosure ofembodiments that include a single inward-facing head-mounted thermalcamera that measures TH_(RBN) (e.g., a single CAM coupled to the bottomof one of the sides of a frame worn by the user) or multiple CAMs (e.g.,multiple CAMs coupled to different locations on a frame worn by theuser). In one example, the at least one CAM includes CAM 681 illustratedin FIG. 37a . In other examples, the at least one CAM may include one ormore of the CAMs described in various figures in this disclosure. Forexample, FIG. 25 illustrates one embodiment of an HMS that may be usedto measure TH_(RBN), in which at least one CAM (four CAMs in this case),is coupled to the bottom of an eyeglasses frame 181. CAMs 182 and 185are used to take thermal measurements of regions on the right and leftsides of the upper lip (186 and 187, respectively), and CAMs 183 and 184are used to take thermal measurements of a region on the user's mouth188 and/or a volume protruding out of the user's mouth. At least some ofthe ROIs may overlap, which is illustrated as vertical lines in theoverlapping areas. Optionally, a CAM from among the one or more of theCAMs includes at least one of the following sensors: a thermopilesensor, and a microbolometer sensor. Optionally, a CAM from among theone or more of the CAMs includes a microbolometer focal-plane array(FPA) sensor or a thermopile FPA sensor. Additional examples of systemsthat include at least one CAM that may be used to take TH_(RBN) areillustrated in FIG. 9 to FIG. 11 as well as FIG. 3b (one or more of thecameras 22, 24, and 28).

In some embodiments, each CAM, from among the at least one CAM, isphysically coupled to frame worn on the head of a user (whosemeasurements are being taken), such as frames of eyeglasses, anaugmented reality HMS, a virtual reality HMS, or a mixed reality HMS. Inone example, each CAM, from among the at least one CAM, is physicallycoupled to frame 680. Optionally, each CAM, from among the at least oneCAM, is located less than 15 cm from the user's face and weighs lessthan 10 g. Optionally, the frame holds each CAM, from among the at leastone CAM, such that the CAM does not protrude beyond the tip of theuser's nose.

In one embodiment, each CAM, from among the at least one CAM, is locatedabove the user's upper lip and less than 15 cm from the user's face, anddoes not occlude any of the user's mouth and nostrils. Optionally,TH_(RBN) include thermal measurements of at least one of first andsecond regions below right and left nostrils (TH_(RBN1) and TH_(RBN2),respectively) of the user, which are indicative of exhale streams fromthe right and left nostrils, respectively. Additionally oralternatively, TH_(RBN) may include thermal measurements of at least oneof a region on the mouth and a volume protruding out of the mouth(TH_(RBN3)) of the user, indicative of exhale stream from the mouth.

The following is a description of one possible utilization of TH_(RBN),which involves calculation of an aerobic activity parameter of a user.FIG. 37a illustrates an embodiment of a system configured to estimate anaerobic activity parameter 688. The system includes at least one CAM (asdescribed above) that is used to measure TH_(RBN) 683 and a computer686. Some embodiments of the system may optionally include additionalelements, such as the frame 680, a head-mounted inward-facing videocamera 682, a sensor 684, and a user interface 689.

The computer 686 is configured, in one embodiment, to calculate, basedon TH_(RBN) (taken by the at least one CAM), the aerobic activityparameter 688. Optionally, the aerobic activity parameter 688 isindicative of one or more of the following values: oxygen consumption(VO₂), maximal oxygen consumption (VO₂ max), and energy expenditure(EE). Optionally, the computer 686 may utilize additional inputs tocalculate the aerobic activity parameter such as measurements of theheart rate (HR) of the user, values of the activity level of the user,and/or various statistics about the user (e.g., age, weight, height,gender, etc.).

Herein, VO₂ refers to a value indicative of the rate of oxygenconsumption. This value typically rises as physical activity becomesmore strenuous and the body has a larger demand for oxygen for variousmetabolic processes. In one example, VO₂ is a value expressed in unitsof mL/(kg·min), or some other units proportional to mL/(kg·min). VO₂ maxrefers to a value indicative of the maximal rate of oxygen consumption;typically, the higher VO₂ max, the higher the person's cardiorespiratoryfitness and endurance capacity during prolonged exercises. EE may referto a value indicative of the rate of energy expenditure, and may beexpressed in various units such as kcal/h, or some other unitproportional to kcal/h. When the rate of energy expenditure isintegrated over a period time, then EE may refer to a value indicativeof the total energy expenditure over the period of time, and may be avalue expressed in calories or some other unit proportional to calories.

Since direct measurements of aerobic activity parameters such as VO₂,VO₂ max, and EE are typically cumbersome uncomfortable procedures thatneed to be performed in controlled settings (e.g., running on atreadmill while wearing a mask that is used to collect and analyzeexhaled breath), these values are often estimated based on variousvalues that are correlated to some extent with the aerobic activityparameters. For example, various formulas and/or models were developedto estimate values of aerobic activity parameters from values such asheart rate (and changes from resting heart rate), activity level, andvarious statistics e.g., age, weight, height, gender, etc.)

Embodiments described herein utilize values indicative of therespiratory activity, such as TH_(RBN) 683 and/or values derived fromTH_(RBN) 683 (e.g., respiration rate and/or respiration volume) in orderto enhance the accuracy of the estimation of aerobic activityparameters. Respiration parameters such as the respiration rate and/orrespiration volume are tightly related to parameters such as VO₂ and EEand thus provide additional information about these parameters.Additionally, respiration values can help reduce inaccuracies inestimation of aerobic activity parameters due to various artifacts. Forexample, during changes in body positions (e.g., postural hypotension),there are usually only minor changes in VO₂ and respiration but majorchanges in HR. In another example, a value such as the respiration ratecan distinguish between non-metabolic (e.g. mental and non-exerciserelated physical stress) and metabolic (physical activity induced)increases in HR. Thus, for example, using respiration data in additionto other values (e.g., HR) may provide better estimations of the valuesof the aerobic activity parameters, compared to estimations that do notinvolve respiration data.

The computer 686 may utilize various approaches in order to estimateaerobic activity parameters based on data that includes TH_(RBN) 683and/or values derived from TH_(RBN). In one embodiment, the computer 686generates feature values based on data comprising TH_(RBN), and utilizesa model 687 to calculate the aerobic activity parameter 688 based on thefeature values. Optionally, the model 687 is trained based on dataindicative of aerobic activity of multiple users (e.g., data thatincludes physiological signals such as respiratory rate, heart rate,etc., of the multiple users). Additionally or alternatively, the model687 is trained based on data that includes previous TH_(RBN) of themultiple users and values of the aerobic activity parameter of themultiple users corresponding to when the previous TH_(RBN) were taken.For example, the training data includes samples, each sample comprising:(i) feature values were generated from certain pervious TH_(RBN) of acertain user taken during certain period of time, and (ii) a labelgenerated based on a measurement of the value of the aerobic activityparameter of the certain user during the certain period of time (i.e.,the value of VO₂, VO₂ max, or EE, as measured during the certain periodof time).

The computer 686 may generate various types of feature values that areused to estimate the value of the aerobic activity parameter 688.Optionally, the computer 686 generates one or more feature values, basedon TH_(RBN) 683, which may be any of the feature values described inthis disclosure that are used to detect a physiological response, and inparticular, the one or more feature values may be any of the featurevalues described in this disclosure as being pertinent to calculation ofa respiratory parameter. Additionally or alternatively, feature valuesgenerated by the computer 686 may include: time series data comprisingvalues measured by a CAM, average values of certain pixels of a CAM,and/or values measured at certain times by the certain pixels.Additionally or alternatively, at least some of the feature valuesgenerated by the computer 686 may include measurements of theenvironment in which the user is in and/or indications of confoundingfactors (e.g., indications of use of medication).

In some embodiments, feature values generated by the computer 686 mayinclude values of one or more respiratory parameters calculated based onTH_(RBN) 683. In one example, the feature values generated by thecomputer 686 include a feature value indicative of a ratio between anextent to which the user breathed via the mouth and an extent to whichthe user breathed via the nose. In another example, the feature valuesgenerated by the computer 686 include a feature value indicative of aratio between durations of exhales of the user and duration of inhalesof the user.

In some embodiment, the feature values generated by the computer 686 mayinclude a feature value indicative of heart rate (HR) of the user whileTH_(RBN) 683 were taken. Additionally or alternatively, the featurevalues generated by the computer 686 include another feature valueindicative of cardiac activity such as heart rate variability (HRV). Forexample, measurements indicative of HR and/or HRV may be obtained by adifferent sensor, which is not a CAM, such as a photoplethysmogram (PPG)sensor that is head-mounted (e.g., coupled to the temple of eyeglassesworn by the user), coupled to a wearable device such as a smartwatch, orembedded in a garment worn by the user, such as a smart shirt.

In addition to data describing physiological signals mentioned above, insome embodiments, data used to generate at least some of the featurevalues by the computer 686 may include various values describing theuser, such as one or more of the following: age, gender, height, weight,type of body build, and body fat percentage. Additionally oralternatively, data used to generate at least some of the feature valuesby the computer 686 may include various values describing an activity ofthe user while TH_(RBN) 683 of the user were taken. Optionally, datadescribing the activity is obtained by sensor 684. In one example, thesensor 684 comprises at least one of an accelerometer and a gyroscope,and the data describing the activity is indicative of at least one ofthe following: cadence, stride length, and/or type of movement (e.g.,walking, running, rowing, cycling, etc.) In another example, the sensor684 comprises a GPS receiver and/or some other sensor that may be usedto determine the user's location. In this example, the data describingthe activity may be indicative of one or more of the following: thespeed of the user's movement, the distance of the user's movement,and/or changes in the user's elevation.

A person's baseline physiological signals, such as resting HR,respiration rate, or blood pressure may be indicative of the aerobicfitness of the person, and may provide useful information forcalculation of an aerobic activity parameter. Thus, in some embodiments,the computer 686 may generate one or more feature values that areindicative of a baseline physiological signal of the user.

How a person's physiological signals change due to physical activity areindicative of the aerobic fitness of the person. Typically, the more fitan individual, the less dramatic the changes in the physiologicalsignals for a certain type of activity. For example, a fit person'srespiration rate will typically increase to a lesser extent after a fewminutes of jogging compared to the increase in respiration that occursto a less fit individual after performing the activity. To capture suchaspects that may reflect on fitness, in some embodiments, the featurevalues generated by the computer 686 may include one or more featurevalues that reflect a change in the values of a physiological signal,before and after a certain extent of activity. For example, a featurevalue may be indicative of the change in the respiratory rate, change tothe respiration volume, or respiration volume after conducting a certainactivity (e.g., five minutes of moderate cycling). In another example, afeature value may be indicative of the change to the heart rate afterrunning at a pace of 12 km/h for five minutes.

In other embodiments, the feature values generated by the computer 686may include one or more feature values that are indicative of athleticperformance of the user. For example, a feature value may be indicativeof the time it took the user to complete a certain exercise such asrunning a mile as fast as the user is capable.

The model 687 is trained on data that includes previous TH_(RBN) of theuser and/or other users. Training the model 687 typically involvesgenerating samples based on the previous TH_(RBN) and correspondinglabels indicative of values of the aerobic activity parameter when theprevious TH_(RBN) were taken. For example, each sample may comprisefeature values generated based on at least some of the previousTH_(RBN), and the sample's label represents the value of the aerobicactivity parameter corresponding to when the at least some of theprevious TH_(RBN) were taken.

In some embodiments, the samples used to train the model 687 includedata pertaining to a diverse set of users comprising users of differentgenders, ages, body builds, and athletic abilities. Optionally, thesamples used to train the model 687 include samples generated based onTH_(RBN) taken at different times of the day, while being at differentlocations, and/or while conducting different activities. In one example,at least some of the samples are generated based on TH_(RBN) taken inthe morning and TH_(RBN) taken in the evening. In another example, atleast some of the samples are generated based on TH_(RBN) of a usertaken while being indoors, and TH_(RBN) of the user taken while beingoutdoors. In yet another example, at least some of the samples aregenerated based on TH_(RBN) taken while a user was sitting down, andTH_(RBN) taken while the user was walking, running, and/or engaging inphysical exercise (e.g., dancing, biking, etc.). Additionally oralternatively, the samples used to train the model 687 may be generatedbased on TH_(RBN) taken while various environmental conditionspersisted. For example, the samples include first and second samplesgenerated based on TH_(RBN) taken while the environment had first andsecond temperatures, with the first temperature being at least 10° C.warmer than the second temperature. In another example, the samplesinclude samples generated based on measurements taken while there weredifferent extents of direct sunlight and/or different extents of windblowing.

Various computational approaches may be utilized to train the model 687based on the samples described above. In one example, a machinelearning-based training algorithm may be utilized to train the model 687based on the samples. Optionally, the model 687 includes parameters ofat least one of the following types of models: a regression model, aneural network, a nearest neighbor model, a support vector machine, asupport vector machine for regression, a naïve Bayes model, a Bayesnetwork, and a decision tree.

In some embodiments, a deep learning algorithm may be used to train themodel 687. In one example, the model 687 may include parametersdescribing multiple hidden layers of a neural network. In oneembodiment, when TH_(RBN) include measurements of multiple pixels, themodel 687 may include a convolution neural network (CNN). In oneexample, the CNN may be utilized to identify certain patterns in thethermal images, such as patterns of temperatures in the region of theexhale stream that may be indicative of respiratory activity, whichinvolve aspects such as the location, direction, size, and/or shape ofan exhale stream from the nose and/or mouth. In another example,calculating a value of an aerobic activity parameter may be done basedon multiple, possibly successive, thermal measurements. Optionally,calculating values of the aerobic activity parameter based on thermalmeasurements may involve retaining state information that is based onprevious measurements. Optionally, the model 687 may include parametersthat describe an architecture that supports such a capability. In oneexample, the model 687 may include parameters of a recurrent neuralnetwork (RNN), which is a connectionist model that captures the dynamicsof sequences of samples via cycles in the network's nodes. This enablesRNNs to retain a state that can represent information from anarbitrarily long context window. In one example, the RNN may beimplemented using a long short-term memory (LSTM) architecture. Inanother example, the RNN may be implemented using bidirectionalrecurrent neural network architecture (BRNN).

Monitoring a user over time can produce many observations indicative ofthe user's fitness. For example, the extent of increase in the user'srespiration rate, change to respiration volume, and/or change in heartrate after moderate running of a few minutes, is indicative of theuser's fitness, and can be measured multiple times. These multipleobservations can be used to estimate the value of an aerobic activityparameter of the user such as VO₂ max (which is also indicative of theuser's fitness) as follows. In one embodiment, the computer 686calculates, based on TH_(RBN) 683, n≥1 values x₁ . . . x_(n), ofobservations of a parameter related to respiration such as therespiration rate, change to respiration rate, respiration volume, changeto respiration volume, and the like. For example, x_(i) may be theincrease to the respiration rate observed after moderate running for aperiod (e.g., five minutes). In another example, x_(i) may be the changeto respiration volume and/or average respiration volume during a halfhour of cycling.

The computer 686 may calculate an estimation of a value of the aerobicactivity parameter (denoted θ*) utilizing one or more probabilityfunctions of the form P(X=x|θ), which is a conditional probability of avalue of the parameter related to respiration given a value of theaerobic activity parameter is equal to θ. Optionally, the computer 686performs at least one of the following in order to calculate θ* (theestimation value of the aerobic activity parameter): a maximumlikelihood (ML) estimation, and a maximum a posteriori probability (MAP)estimation.

The one or more probability functions of the form P(X=x|θ) may becalculated based on data pertaining to a diverse set users comprisingusers of different genders, ages, body builds, and athletic abilities.Optionally, the data includes observations of a parameter related torespiration calculated based on TH_(RBN) of the users. In oneembodiment, a probability function of the form P(X=x|θ) may be a tabledescribing the probability of observing different values of x given acertain value of θ. For example, the table may describe an empiricallyobserved probabilities for various increases in respiration (e.g.,increases of 2, 4, 6, . . . , 40 breaths per minute) given differentvalues of θ, such as VO₂ max=10, 15, 20, . . . , 75, 80 mL/(kg·min). Inanother embodiment, a probability function of the form P(X=x|θ) may bedescribed by a model that includes parameters of the distribution, wherethe parameters may be set using various approaches such as regressionand/or maximum entropy approaches. Optionally, the parameters of theprobability function describe a continuous exponential distribution.

A user interface 689 may be utilized to present the aerobic activityparameter 688 and/or present an alert related to the aerobic activityparameter 688. In one example, user interface 689 may be used to alertthe user responsive to an indication that the aerobic activity parameterhas fallen below a threshold (e.g., when the rate of energy expenditurefalls below a threshold) or when the aerobic activity parameter reachesa certain threshold (e.g., when the total energy expenditure during asession reaches a certain caloric goal). Optionally, the user interface689 includes a display, such as the display of a smart phone, asmartwatch, or a head-mounted augmented reality display. Optionally, theuser interface 689 includes a speaker, such as a speaker of a smartphone, a smartwatch, or a head-mounted augmented reality display, or aspeaker of a pair of headphones or an earbud.

As discussed herein, thermal measurements indicative of an exhale streammay be used by a computer to calculate various respiratory parameters,aerobic activity parameters, and coaching indications. In order toproduce a better signal regarding the user's respiratory activity, insome embodiments, the computer 686 (or the computer 696 discussed below)may utilize additional input sources (besides thermal cameras).

In some embodiment, the additional input sources may include one or morevisible-light cameras that capture images indicative of respiratoryactivity. In one example, the additional input sources include at leastone inward-facing head-mounted visible-light camera (e.g., the camera682), which is configured to take images of a region on the mouth(IM_(M)) of the user. In this example, IM_(M) are indicative of whetherthe mouth is open or closed. In another example, the additional inputsources include at least one inward-facing head-mounted visible-lightcamera configured to take images of a region on the nose (IM_(N)) of theuser; in this example, IM_(N) are indicative of movement of the nosewhile the user inhales (in this example, the camera 682 may beconfigured to take images of the nose in addition to, or instead of, theimages of the mouth). Optionally, calculating various values (e.g.,breathing rate, an aerobic activity parameters, or a coachingindication) based on IM_(M) and/or IM_(N) involves generating featurevalues based on IM_(M) and/or IM_(N) and using them in the calculationof said values (e.g., in addition to feature values generated based onTH_(RBN)). For example, feature values generated based on IM_(M) and/orIM_(N) involve using various image processing techniques and representvarious low-level image properties. Some examples of such features mayinclude features generated using Gabor filters, local binary patternsand their derivatives, features generated using algorithms such as SIFT,SURF, and/or ORB, and features generated using PCA or LDA. Optionally,IM_(M) and/or IM_(N) may be used to identify different states of theuser (e.g., open vs. closed mouth or movement of the nostrils), and theinformation regarding the different states may be used as input (e.g.,feature values) when calculating parameters such as the breathing rate.

In other embodiments, the additional input sources may include one ormore microphones configured to record sounds made by the user'srespiration. For example, the one or more sensors may includemicrophones in right and/or left in-the-ear earbuds, and feature valuesmay be generated based on audio signal analysis of the recordings fromthe earbuds and utilized to calculating parameters such as the breathingrate, to detect inhaling/exhaling events, etc. Optionally, such in-earmeasurements are used to calculate the user's breathing rate while theuser was walking or running in an environment having ambient noise levelabove 50 dBA.

Other examples or sensors that may be used as additional input sourcesinclude sensors physically that are coupled to a garment worn over theuser's torso and comprises at least one of the following: a pressuresensor, a stretch sensor, an electromechanical sensor, and a radioreceiver. Optionally, these sensors are configured to measure movementsof the chest due to respiration activity of the user, and thesemeasurements are utilized to calculate various parameters such as thebreathing rate.

The additional input sources described above may serve, in someembodiments, as complementary data that enhance accuracy of respiratorysignals detected based on TH_(RBN). For example, in some embodiments,exhaling air produces a stronger thermal signal than inhaling air. Inthese embodiments, detection of inhalation events can be assisted byimages of the nostrils (which often show distinct movement wheninhaling) In another example, there may be conditions in which exhalingmay produce a relatively weak thermal signal, e.g., when exercising inwarm environments in which the temperature of the exhaled air is closeto the temperature in the environment. In such cases, additional data,such as data from sensors embedded in a garment or microphones inearbuds, may help and provide better indications of breathing.

The following is a description of another possible utilization ofTH_(RBN), which involves virtual coaching based on respiration data.FIG. 37b illustrates an embodiment of an athletic coaching system. Thesystem includes at least one CAM (as described above) that is used tomeasure TH_(RBN) 693 and a computer 696. Some embodiments of the systemmay optionally include additional elements, such as the frame 680, ahead-mounted inward-facing video camera 692, a sensor 694, and a userinterface 699.

The computer 696 is configured, in some embodiments to: receivemeasurements of movements (M_(move) 695) involving the user; generate,based on TH_(RBN) 693 and M_(move) 695, a coaching indication 698; andpresent, via a user interface 699, the coaching indication 698 to theuser. Various virtual coaching applications may be realized by analyzingTH_(RBN) 693 and M_(move) 695, and providing the user with insightsand/or instructions based on the analysis. These insights and/orinstructions may assist to improve the user's athletic performance invarious ways.

One type of coaching application built on the system illustrated in FIG.37b provides insights and/or instructions to a user performing anathletic activity that involves an aerobic exercise with repetitivemotions such as running, rowing, or cycling. In one embodiment, thecomputer 696 generates the coaching indication 698, which is indicativeof a change the user should make to one or more of the following:cadence of movements, stride length (if the user is running), breathingrate, breathing type (mouth or nasal), and duration of exhales.Optionally, responsive to a determination that the change is needed, thecomputer 696 provides, via the user interface 699, an indication to theuser of this fact. For example, the user interface 699 may include aspeaker (e.g., in earbuds) and the computer 696 generates an audioindication (e.g., a certain sound effect such as beeping at a certainfrequency and/or speech conveying the coaching indication 698). Inanother example, the user interface may include a display and thecoaching indication may be provided via visual cues (e.g., text, animage, or a light flashing at a certain frequency). The following arevarious examples of computations that may be performed by the computer696 in order to generate the coaching indication 698.

In one embodiment, the computer 696 calculates the breathing rate of theuser based on TH_(RBN) 693 and then checks if it is in a desired range.Responsive to the breathing rate being below a first threshold, thecomputer 696 includes in the coaching indication 698, an instruction toincrease the breathing rate. Additionally or alternatively, responsiveto the breathing rate being above a second threshold (which is higherthan the first threshold), the computer includes in the coachingindication 698 an instruction to decrease the breathing rate.Optionally, the first and/or second thresholds are calculated based onM_(move) 695. For example, the first threshold (minimal desiredbreathing rate) and/or the second threshold (maximal desired breathingrate) are set according to the level of activity of the user.Optionally, “level of activity” may refer to one or more of thefollowing: the speed of the user (e.g., when running or cycling), thecadence of the user's movement, a value of an aerobic activity parameterof the user (e.g., VO₂ or EE).

In another embodiment, the computer 696 calculates a value indicative ofthe cadence of the user based on M_(move) 695. For example, the computer696 may identify cyclic signals indicating movement such as pedaling,rowing, or strides. Optionally, the computer 696 utilizes a machinelearning model to calculate the cadence based on M_(move) 695, where themodel is trained based on M_(move) of other users. Optionally,responsive to the cadence being below a first threshold, the computer696 includes in the coaching an indication 698 instruction to increasethe cadence. Additionally or alternatively, responsive to the cadencebeing above a second threshold, the computer 696 includes in thecoaching indication 698 an instruction to decrease the cadence.Optionally, the first and/or second thresholds are calculated accordingto TH_(RBN) 693. For example, the first and/or second thresholds maycorrespond to a desired cadence that is appropriate for the breathingrate of the user, as determined based on TH_(RBN) 693.

In yet another example, the computer 696 calculates a value indicativeof exhale durations of the user based on TH_(RBN) 693. Optionally, thecomputer 696 includes in the coaching indication 698 an instruction toincrease the exhale durations responsive to determining that the exhaledurations are below a threshold. Optionally, the threshold is calculatedbased on at least one of M_(move) 695 and TH_(RBN) 693. For example, thethreshold may be set according to a predetermined function that assignsa minimal desired duration of exhales based on the cadence or speed ofthe user (e.g., as determined based on M_(move) 695) and/or based on thebreathing rate of the user (e.g., as determined based on TH_(RBN) 693).

In still another embodiment, the computer 696 detects, based on TH_(RBN)693, whether the user is breathing through the mouth. Responsive todetecting that the user is breathing through the mouth, the computer 696includes in the coaching indication 698 an instruction to the user tobreathe through the nose.

The computer 696 may utilize a machine learning model 697 to generatethe coaching indication 698. In some embodiments, the computer generatesfeature values based on TH_(RBN) 693 and/or M_(move) 695. For example,the feature values may include one or more of the feature valuesdescribed above which are generated based on TH_(RBN) 683 and/ormeasurements of the sensor 684 and are used to estimate the aerobicactivity parameter 688. Optionally, the computer 696 utilizes the model697 to calculate, based on the feature values generated based onTH_(RBN) 693 and/or M_(move) 695, a value indicative of whether thechange is needed and/or what change in the user's activity should beindicated in the coaching indication 698.

The model 697 may be generated based on data comprising previously takenTH_(RBN) and M_(move) of the user and/or other users and indications ofappropriate coaching instructions (and whether coaching instructions areneeded) corresponding to the time previously taken TH_(RBN) and M_(move)were taken. For example, the previously taken TH_(RBN) and M_(move) maybe used to generate samples; each sample comprising feature valuesgenerated based on TH_(RBN) and M_(move) taken during a certain period(the same type of feature values generate by the computer 696, asdescribed above). The indications on appropriate coaching instructionsmay be used to create labels for the samples. In one example, thecoaching instructions are provided by a human annotator (e.g., a humancoach) that reviews the data and determines whether changes could bemade to improve the athletic performance. In another example, thecoaching instructions are provided by an expert system (e.g., a rulebased system such as a decision tree), which is designed for thispurpose.

In some embodiments, M_(move) 695 are generated by a sensor 694, whichmay represent herein one or more of various types of sensors. In oneembodiment, the sensor 694 is an accelerometer and/or gyroscope in adevice carried or worn by the user. For example, the sensor 694 may be amovement sensor in a smartwatch or smart glasses worn by the user or amovement sensor in a smartphone carried by the user. Optionally,analysis of measurements of the sensor 694 provides information aboutone or more of the following: the types of movements the user is making(e.g., running, cycling, or rowing), the cadence of the user (e.g.,number of steps per minute, number of revolutions per minute in cycling,or the number of strokes per minute), and/or the speed of the user. Inanother embodiment, the sensor 694 is a location identifying sensor,such as a GPS receiver. Optionally, analysis of measurements of thesensor 694 provides information on the speed of the user, the elevationand/or distance traveled, etc. In some embodiments, M_(move) 695 mayinclude information obtained from multiple movement sensors. In oneexample, information about the speed and/or distance traveled by theuser, coupled with information about the cadence, is used in order todetermine the length of the user's strides.

Another type of coaching application that may utilize TH_(RBN) 693 andM_(move) 695 provides the user with breathing cues (e.g., a breathingpacer application) in order to assist the user to breathe at a desiredpace while conducting athletic activity. In one embodiment, the computer696 calculates a target breathing rate based on data comprising at leastone of TH_(RBN) 693 and M_(move) 695, and includes in the coachingindication breathing cues that correspond to the target breathing rate.Optionally, the computer 696 receives a value indicative of the heartrate (HR) of the user and uses HR to calculate the target breathing rate(in addition to utilizing at least one of TH_(RBN) 693 and M_(move)695). In one example, M_(move) 695 is utilized to calculate a valueindicative of the speed of the user and/or the cadence of the user, andthe computer 696 utilizes a predetermined function to select for theuser the target breathing rate, based on the speed and/or the cadence.In another example, a current breathing rate of the user, which iscalculated based on TH_(RBN) 693, is used to select a target breathingrate that will match the cadence of the user (e.g., which is determinedbased on M_(move) 695). In still another example, TH_(RBN) 693 andM_(move) 695 are used as input to a function that calculates the targetbreathing rate. For example, the computer 696 may generate featurevalues (e.g., as discussed above with respect to the coaching indicationregarding an instruction to change an aspect of the user's activity) andutilize a certain model to calculate, based on these feature values, thetarget breathing rate. Optionally, the certain model is generated basedon data comprising previously taken TH_(RBN) and M_(move) of the userand/or other users and indications of the appropriate breathing rate asdetermined by an expert (e.g., a human or an expert system). The variouscomputational approaches described herein with respect to detecting aphysiological response may be employed in order to calculate the targetbreathing rate (e.g., comparison to threshold, reference time series,and/or machine learning approaches described herein).

In one embodiment, the computer 696 calculates a current breathing ratebased on TH_(RBN) 693 and compares the current breathing rate to firstand second thresholds, where the first threshold is below the targetbreathing rate and second threshold is above the target breathing rate.Responsive to the current breathing rate being below the first thresholdor above the second threshold, the computer 696 instructs the userinterface 699 to start providing the breathing cues or to increaseintensity of provided breathing cues. Optionally, responsive to thecurrent breathing rate being above the first threshold and below thesecond threshold, for at least a certain duration, the computer 696instructs the user interface 699 to cease from providing the breathingcues or to provide weaker breathing cues.

The breathing cues may be provided in various ways. In one example, theuser interface 699 includes a speaker (e.g., in an earbud) and thebreathing cues comprise auditory cues that have a frequency thatcorresponds to the target breathing rate (e.g., a beeping sound at thefrequency or a music that has an underlying beat at the frequency). FIG.37c illustrates a cycler who receives breathing cues via an earbud,which correspond to the target breathing rate. In another example, theuser interface 699 includes a display (e.g., a display of augmentedreality smart glasses, and the breathing cues comprise visual cues thathave a frequency that corresponds to the target breathing rate (e.g., aflashing light or icon that changes its size at a frequency thatcorresponds to the target breathing rate).

Yet another type of coaching application that may utilize TH_(RBN) 693and M_(move) 695 a coaching indication indicative synchronization of abreathing pattern of the user with a sequence of movements of the user.Optionally, the coaching indication 698 may be indicative of whether thebreathing is synchronized with a sequence of movements (i.e., indicateto the user whether the user is breathing correctly when performing thesequence of movements). Additionally or alternatively, the coachingindication 698 may provide cues of the correct breathing patterncorresponding to the sequence of movements (i.e., provide cues thatindicate to the user a synchronized breathing pattern). In oneembodiment, the computer 696 provides the user, via the user interface699, an indication indicative of whether the user's breathing issynchronized with the sequence of movements. Additionally oralternatively, the computer 696 determine whether the user did notbreathe in an appropriate pattern while performing a sequences ofmovements. Responsive to determining that the user did not breathe inthe appropriate pattern, the computer 696 notifies the user of this factvia a user interface 699.

A “correct” breathing pattern refers to a breathing pattern that isconsidered appropriate for the sequence of movements, and thus may beconsidered synchronized with the sequence of movements. Optionally,determining a breathing pattern that is correct for a sequence ofmovements may be done based on expert knowledge (e.g., coaches, expertsin athletics and physiology, etc.) Additionally or alternatively,correct breathing patterns for a sequence of movements may be learnedfrom observations. For example, performance of one or more users may bemonitored while they breathe in various patterns while performing acertain sequence of movements, and the optimal breathing pattern (i.e.,the breathing pattern that is synchronized with the certain sequence)may be determined based on detecting a breathing pattern for which theperformance is maximized (e.g., farthest/most accurate driver hit).

The sequence of movements performed by the user may be, in someembodiments, sequences involved in performing a specific operation, suchas swinging a bat, a racket or a golf club, lifting weights, performinga move in yoga, etc. In such cases, various sensors may be utilized inorder to obtain M_(move) 695, which provide indications of the type ofmovements the user is performing and/or how the user is manipulatesobjects (such as a bat, a racket, a golf club, a barbell, etc.). In someembodiments, the sensor 694 is a camera that takes images of the user,the user's limbs, and/or objects held by the user. In one example, thesensor 694 is an outward-facing head-mounted camera (e.g., a camerapointed outwards that is coupled to a frame worn on the user's head). Inanother example, the sensor 694 is an external camera, such as a camerain a laptop, smart TV, or a webcam. Optionally, the computer 696performs image analysis of M_(move) 695 that includes images taken bythe sensor 694 in order to identify various movements of the user.

In some embodiments, the sensor 694 may include at least one of LiDARsystem and a RADAR system. Optionally, the computer 696 analyzesM_(move) 695 in order to identify movements of the user's limbs, changesto the user's pose, and/or the location of an object held by the user(e.g., a barbell, racket, golf club, etc.)

The following are examples of various sequences of movements andcoaching indications that may be generated for them based on TH_(RBN)693 and M_(move) 695.

In one embodiment, a sequence of movements of the user corresponds to apressing motion of weights or a barbell, and the coaching indication 698indicates to inhale in the concentric phase of the press and exhale inthe eccentric phase of the press. In one example, the sensor 694 is amovement sensor (e.g., an accelerometer embedded in a garment worn bythe user) and the computer 696 analyzes M_(move) 695 to identifydifferent movements involved in the pressing motion. In another example,the sensor 694 is an outward-facing head-mounted camera or a cameraexternal to the user, and M_(move) 695 include images of the user and/orof the weights or barbell. In this example, the computer 696 may utilizeimage analysis of M_(move) 695 in order to identify different movementsinvolved in the pressing motion. Optionally, the coaching indication 698is provided to the user while the user performs the sequence ofmovements, such that when the computer 696 recognizes that the user isabout push the weights or barbell, or starts to push (initiating theconcentric phase), the user is instructed, in the coaching indication698, to exhale.

In another embodiment, a sequence of movements of the user correspondsto swinging a racket in order to hit a ball with the racket (e.g., whileplaying tennis), and the coaching indication 698 indicates to exhalewhile hitting the ball. In one example, the sensor 694 is a movementsensor (e.g., an accelerometer) on the user's body, and the computer 696analyzes M_(move) 695 to identify movements that characterize a swingingmotion. In another example, the sensor 694 comprises at least one of aLiDAR system and a RADAR system, and the computer 696 analyzes M_(move)695 to determine the location of the arms and/or the racket relative tothe user's body in order to identify the swinging motion. Optionally,the coaching indication 698 is provided to the user while the userperforms the sequence of movements, such that when the computer 696recognizes that the user is about swing the racket, or starts to startsto swing the racket, the user is instructed, in the coaching indication698, to exhale.

In yet another embodiment, a sequence of movements of the usercorresponds to making a drive shot in golf, and the coaching indication698 indicates to inhale during the backswing and exhale again on thedownswing. Optionally, the coaching indication 698 also indicates toexhale at address. Optionally, the coaching indication 698 is providedto the user while the user performs the sequence of movements, such thatwhen the computer 696 recognizes that the user is about to drive theshot (e.g., based on characteristic movements in the address), or startsthe drive shot (e.g., by starting the backswing), the user isinstructed, in the coaching indication 698, to exhale. FIG. 37dillustrates a user receiving coaching instructions while hitting a drivein golf; the figure illustrates the user receiving instructions (e.g.,via an earbud) to inhale on the backswing and exhale of the downswing.

In still another embodiment, the computer 696: (i) receives from afitness app (also known as a personal trainer app) an indication thatthe user should exhale while making a movement, (ii) determines, basedon m_(move) 695, when the user is making the movement, and (iii)determines, based on TH_(RBN) 693, whether the user exhaled while makingthe movement. Optionally, the computer 696 commands the user interface699 to (i) play a positive feedback in response to determining that theuser managed to exhale while making the physical effort, and/or (ii)play an alert and/or an explanation why the user should try next time toexhale while making the physical effort in response to determining thatthe user did not exhale while making the physical effort. FIG. 28aillustrates a fitness app running on smartphone 196, which instructs theuser to exhale while bending down. CAM coupled to eyeglasses frame 181measures the user breathing and is utilized by the fitness app thathelps the user to exhale correctly. FIG. 28b illustrates inhaling whilestraightening up.

There are various ways in which the computer 696 may generate a coachingindication that is indicative of synchronization of a breathing patternof the user with a sequence of movements of the user. In someembodiments, generating the coaching indication 698 involves identifyinga breathing pattern based on TH_(RBN) 693 and/or the sequence ofmovements based on M_(move) 695. Additionally or alternatively, amachine learning model may be used to calculate, based on TH_(RBN) 693and M_(move) 695, a value indicative of an extent to which the breathingpattern of the user is synchronized with the sequence of movements.

In some embodiments, a breathing pattern may refer to a description ofcharacteristics of the user's breathes during a certain period of time.Optionally, the breathing pattern is determined by identifying, based onTH_(RBN) 693, times at which the user inhaled or exhaled and/or bycalculating, based on TH_(RBN) 693, one or more of the variousrespiratory parameters described herein. Optionally, a breathing patternmay describe one or more of the following values: times at which theuser inhaled, times at which the user exhaled, durations of inhales,durations of exhales, respiration volume (or changes to the respirationvolume), indications of whether the user exhaled and/or inhaled from themouth, and indications of whether the user exhaled and/or inhaled fromthe nose. Optionally, the values comprised in a breathing patterninclude corresponding temporal values. For example, a breathing patternmay include the following: at time t=0 inhaling, at time t=1.5 exhaling,at time t=3 inhaling, etc. Additionally or alternatively, a breathingpattern may include qualitative descriptors of respiration (determinedbased on TH_(RBN) 693). For example, a breathing pattern may include thefollowing descriptors: a regular inhaling followed by a short burstyexhaling (e.g., when hitting a ball).

In some embodiments, a sequence of movements of the user may refer tovalues describing movement of the user's body (changing location in the3D space) and/or changes to pose and/or orientation of limbs.Optionally, the sequence of movements may be represented usingdescriptors that represent specific movements that are identified basedon M_(move) 695. For example, the sequence of movements describing adrive shot in golf may include descriptors such as: getting intoposition (address), a backswing, and a downswing. Optionally, thedescriptors of movements in a sequence of movements may have associatedtemporal values describing properties such as when each of the movementsstarted and/or how long each of the movements lasted.

In one embodiment, identifying a certain movement, from among thesequence of movements, is done using a machine learning-based model.M_(move) 695 (or a portion thereof, e.g., a segment lasting a second)are converted into feature values representing values of M_(move) (e.g.,values of an accelerometer, low-level image features, etc.), usingapproaches described herein and/or approaches known in the art. A modelis utilized to calculate, based on the feature values, a valueindicative of whether the user performed the certain movement.Optionally, the model is trained on samples of one or more users, eachcomprising feature values generated based on M_(move) of a user takenwhile said user performed the certain movement. Optionally, the model istrained on samples of one or more users, each comprising feature valuesgenerated based on M_(move) of a user taken while said user did notperform the certain movement.

In one embodiment, identifying a certain movement, from among thesequence of movements, is done using similarity to reference M_(move)taken while a certain user performed the certain movement. M_(move) 695(or a portion thereof, e.g., a segment lasting a second), is compared tothe reference M_(move) and if the similarity reaches a threshold, theuser is considered to have performed the certain movement while M_(move)695 (or the portion thereof) were taken. In one example, the segments ofM_(move) being compared are treated as time series data, and one or moreof the methods referenced herein with respect to determining similarityof time series are used to determine the similarity. In another example,the segments of M_(move) being compared may be represented as points ina high dimensional space, and a distance function such as the Euclidiandistance or some other distance function is used find the distancebetween the points. The threshold, to which the similarity is compared,may be determined experimentally and selected in order to achieve adesirable balance between specificity and sensitivity of identificationsof the certain movement.

In order to determine to what extent the sequence of movements(determined based on M_(move) 695) is synchronized with the breathingpattern (determined based on TH_(RBN) 693) the computer 696 may alignthe sequence of movements and breathing pattern (e.g., by using temporalinformation associated with both). This alignment may be done indifferent ways. In one example, the alignment determines whichrespiratory actions were performed when different movements of thesequence of movements were performed. In this example, the computer 696may utilized the alignment to determine whether the respiratory actionscorrespond to one or more predetermined breathing patterns appropriatefor the certain movement sequence.

In another embodiment, feature values are generated based on thesequence of movements and the breathing pattern. For example, some ofthe feature values may describe which movements were performed, theirrelative order, and timing Additionally some feature values may describewhich respiratory activities were performed, theirorder/timing/duration, and other related properties described above. Amodel is used to calculate, based on the feature values, a valueindicative of the extent to the breathing pattern is synchronized withthe sequence of movements. Optionally, the model is trained basedsamples generated from M_(move) and TH_(RBN) of one or more users, whichinclude samples generated based on M_(move) and TH_(RBN) taken while thesequence of movements was performed and a user was breathing in abreathing pattern that was synchronized with the sequence of movements.Additionally, the samples used to train the model may include samplesgenerated based on M_(move) and TH_(RBN) taken while the sequence ofmovements was performed and a user was not breathing in a breathingpattern that was synchronized with the sequence of movements.Optionally, a breathing pattern is considered not to be synchronizedwith a sequence of movements if the extent of the synchronizationbetween the two is below a predetermined threshold (and consideredsynchronized otherwise).

In yet another embodiment, a unified sequence is created from thebreathing pattern and the sequence of movements, which describes bothmovements and respiration activities. For example, the sequence ofmovements and the breathing pattern may be merged to a single sequenceusing temporal data. This unified sequence may then be evaluated todetermine whether it corresponds to a breathing pattern that issynchronized to a sequence of movements based on similarity to areference unified sequence and/or a machine learning-based model trainedon samples generated based on unified sequences that are generated basedon M_(move) and TH_(RBN) taken while the sequence of movements wasperformed and a user was breathing in a breathing pattern that wassynchronized with the sequence of movements.

In some embodiments, determining whether a breathing pattern issynchronized with a sequence of movements of the user is done using amachine learning-based model. The computer 696 generates feature valuesbased on TH_(RBN) 693 (e.g., one or more feature values of typesdescribed herein which are used to calculate a respiratory parameter)and/or based on M_(move) 695 (e.g., feature values described above whichare used to identify certain movements). The computer 696 utilizes themachine learning-based model to calculate, based on the feature values,a value indicative of whether the breathing pattern was synchronizedwith the sequence of movements. Optionally, the model was trained basedon data comprising: a first set of previous TH_(RBN) and M_(move) of oneor more users, taken while performing the sequence of movements andbreathing in a pattern that is synchronized with the sequence ofmovements, and a second set of previous TH_(RBN) and M_(move) the one ormore users taken while performing the sequence of movements andbreathing in a pattern that is not synchronized with the sequence ofmovements.

FIG. 38 illustrates one embodiment of a system configured to provideneurofeedback (based on measurements of thermal camera 720) and/orbreathing biofeedback (based on measurements of at least one of thermalcameras 723, 725, 727 and 729). Thermal camera 720 takes thermalmeasurements of a region on the forehead 721, thermal cameras 723 and725 take thermal measurements of regions on the right and left sides ofthe upper lip, respectively, and thermal cameras 727 and 729 takethermal measurements of regions on the user's mouth and/or volumesprotruding out of the user's mouth. The thermal cameras are physicallycoupled to a frame 731 that may be part of an augmented-realty system inwhich the visual feedback of the breathing biofeedback and/orneurofeedback is presented to the user via UI 732. The system maycontrol the breathing biofeedback and/or neurofeedback session based onmeasurements taken by additional sensors, such as (i) sensor 722, whichmay be an outward-facing thermal camera that measures the intensity ofinfrared radiation directed at the face, and (ii) thermal cameras 724and 726 that measure regions on the right and left periorbital areas,respectively.

In one embodiment, a system configured to provide a breathingbiofeedback session for a user includes at least one inward-facinghead-mounted thermal camera (CAM) and a user interface (UI). The atleast one CAM takes thermal measurements of a region below the nostrils(TH_(ROI)), and TH_(ROI) are indicative of the exhale stream. The UIprovides feedback, calculated based on TH_(ROI), as part of a breathingbiofeedback session for the user. Optionally, the breathing biofeedbacksystem may include additional elements such as a frame, a computer,additional sensors, and/or thermal cameras as described below.

The at least one CAM may have various configurations. In a firstexample, each of the at least one CAM is located less than 15 cm fromthe user's face and above the user's upper lip, and does not occlude anyof the user's mouth and nostrils. Optionally, TH_(ROI) include thermalmeasurements of at least first and second regions below right and leftnostrils of the user. Optionally, the at least one CAM consists of asingle CAM.

In a second example, the system further includes a frame worn on theuser's head. TH_(ROI) include thermal measurements of first and secondregions below right and left nostrils (TH_(ROI1) and TH_(ROI2),respectively) of the user. The at least one CAM includes first andsecond thermal cameras for taking TH_(ROI1) and TH_(ROI2), respectively,which are located less than 15 cm from the user's face and above thenostrils. The first thermal camera is physically coupled to the righthalf of the frame and captures the exhale stream from the right nostrilbetter than it captures the exhale stream from the left nostril, and thesecond thermal camera is physically coupled to the left half of theframe and captures the exhale stream from the left nostril better thanit captures the exhale stream from the right nostril.

In a third example, TH_(ROI) include thermal measurements of first,second and third regions on the user's face, which are indicative ofexhale streams from the right nostril, the left nostril, and the mouth,respectively. The first and second regions are below the right and leftnostrils, respectively, and the third region includes the mouth and/or avolume protruding out of the mouth.

The UI provides the feedback for the user during the breathingbiofeedback session. The UI may also receive instructions from the user(e.g., verbal commands and/or menu selections) to control the sessionparameters, such session duration, goal, and type of game to be played.The UI may include different types of hardware in different embodiments.Optionally, the UI includes a display that presents the user with videoand/or 3D images, and/or a speaker that plays audio. Optionally, the UIis part of a device carried by the user. Optionally, the UI is part of aHMS to which the at least one CAM is coupled. Some examples of displaysthat may be used in some embodiments include a screen of a handhelddevice (e.g., a screen of a smartphone or a smartwatch), a screen of ahead-mounted device (e.g., a screen of an augmented reality system or avirtual reality system), and a retinal display. In one embodiment, theUI may provide tactile feedback to the user (e.g., vibrations).

In some embodiments, at least some of the feedback presented to the uservia the UI is intended to indicate to the user whether, and optionallyto what extent, the user's breathing (as determined based on TH_(ROI))is progressing towards a target pattern. The feedback may be designed toguide the user to breathe at his/her resonant frequency, which maximizeamplitude of respiratory sinus arrhythmia and is in the range of 4.5 to7.0 breaths/min.

The feedback may indicate the user's progress towards the target indifferent ways, which may involve visual indications, audio indications,and/or tactile indications. In one embodiment, the user is provided witha visual cue indicating the extent of the user's progress. For example,an object may change states and/or locations based on how close the useris to the target, such as an image of a car that moves forward as theuser advances towards the target, and backwards if the user regresses.In one example, the feedback may include an audio-visual video of a fishthat swims to the left when the exhale becomes smoother and stopsswimming or even swims to the right when the exhale becomes less smooth.In another embodiment, the user is provided with an audio cue indicatingthe extent of the user's progress. For example, music played to the usermay change its volume, tune, tone, and/or tempo based on whether theuser is advancing towards the target or regressing from it, and/ordifferent music pieces may be played when the user is at different ratesof progression. In still another embodiment, the user is provided with atactile cue indicating the extent of the user's progress. For example, adevice worn and/or carried by the user may vibrate at differentfrequencies and/or at different strengths based on how far the user isfrom a goal of the session.

Breathing biofeedback requires closing the feedback loop on a signalthat changes fast enough. Smoothness of the exhale stream, the shape,and/or the BRV have components that change at frequency above 2 Hz,which may be fast enough to act as the parameter on which the breathingbiofeedback loop is closed. The feedback may be calculated and presentedto the user at frequencies higher than 1 Hz, 2 Hz, 5 Hz, 10 Hz, 20 Hzand/or 40 Hz (which are all higher than the user's breathing rate).

The computer calculates, based on TH_(ROI), a characteristic of theuser's breathing, and generates the feedback based on thecharacteristic. Some breathing characteristics may be difficult tocontrol, and often people are not even aware of them. However, breathingbiofeedback can help the user achieve awareness and/or gain control overhis/her breathing, and as a result improve the user's state.

One characteristic of the breathing, which the computer may take intoaccount when controlling the breathing biofeedback session, is thesmoothness of the exhale stream. Optionally, the smoothness of theexhale stream refers to a mathematical property of sets of values thatinclude values of TH_(ROI) taken over a period of time (e.g., values ina window that includes a portion of a breath, or even one or morebreaths). The smoothness may be considered a property of graphs of thesets of values, and may represent how much of a variance there is inthese values when compared to an average trend line that corresponds tothe breathing. As discussed above, the smoothness may be calculated invarious ways such as using Fourier transform and/or measuring a fit to alow order polynomial.

In one embodiment, the feedback is indicative of similarity betweencurrent smoothness of the exhale stream and target smoothness of theexhale stream. The current smoothness is calculated in real-time basedon TH_(ROI), and the target smoothness is calculated based on previousTH_(ROI) of the user taken while the user was in a state consideredbetter than the user's state while starting the breathing biofeedbacksession. Optionally, the similarity may be formulated as the distancebetween the current smoothness and the target smoothness.

In one embodiment, the feedback is indicative of at least one of thefollowing: whether the smoothness is above or below a predeterminedthreshold, and whether the smoothness has increased or decreased since aprevious feedback that was indicative of the smoothness. Optionally, thesmoothness is calculated at frequency≥4 Hz, and the delay from detectinga change in the smoothness to updating the feedback provided to the useris ≤0.5 second. As another option, the feedback may be indicative ofwhether the smoothness is above or below the predetermined threshold,and the user interface may update the feedback provided to the user at arate≥2 Hz.

Another characteristic of the breathing, which the computer may takeinto account when controlling the breathing biofeedback session, is theshape of the exhale stream (SHAPE). Optionally, the SHAPE is describedby one or more parameters that represent a 3D shape that bounds theexhale stream that flows from one or both of the nostrils. Optionally,the feedback is indicative of whether the SHAPE matches a predeterminedshape, and/or whether the SHAPE has become more similar or less similarto the certain shape since a previous feedback that was indicative ofthe SHAPE. In one embodiment, the feedback is indicative of similaritybetween current shape of the exhale stream (SHAPE) and target SHAPE,wherein the current SHAPE is calculated in real-time based on TH_(ROI),and the target SHAPE is calculated based on at least one of thefollowing: (i) previous TH_(ROI) of the user taken while the user was ina state considered better than the user's state while starting thebreathing biofeedback session, and (ii) TH_(ROI) of other users takenwhile the other users were in a state considered better than the user'sstate while starting the breathing biofeedback session.

Another characteristic of the breathing, which the computer may takeinto account when controlling the breathing biofeedback session, is thebreathing rate variability (BRV), which is indicative of the variationsbetween consecutive breathes. Optionally, the feedback may be indicativeof similarity between current breathing rate variability (BRV) and atarget BRV, wherein the current BRV is calculated in real-time based onTH_(ROI), and the target BRV is calculated based on previous TH_(ROI) ofthe user taken while the user was in a state considered better than theuser's state while starting the breathing biofeedback session.Additionally or alternatively, the feedback may be indicative of whetherthe BRV is above or below a predetermined threshold, and/or whether apredetermined component of the BRV has increased or decreased since aprevious feedback that was indicative of the BRV.

Similarly to how heart rate variability (HRV) is calculated, there arevarious computational approaches known in the art that may be used tocalculate the BRV based on TH_(ROI). In one embodiment, calculating theBRV involves identifying matching events in consecutive breaths (such asstart exhaling, exhale peak, and/or inhale peak), and analyzing thevariability between these matching events. In another embodiment, theuser's breathing is represented as time series data from which lowfrequency and high frequency components of the integrated power spectrumwithin the time series signal are extracted using Fast Fourier Transform(FFT). A ratio of the low and high frequency of the integrated powerspectrum within these components is computed and analysis of thedynamics of this ratio over time is used to estimate the BRV. In stillanother embodiment, the BRV may be determined using a machinelearning-based model. The model may be trained on samples, eachincluding feature values generated based on TH_(ROI) taken during acertain period and a label indicative of the BRV during the certainperiod.

In some embodiments, the computer calculates a value indicative ofsimilarity between a current TH_(ROI) pattern and a previous TH_(ROI)pattern of the user taken while the user was in a target state, andgenerates the feedback based on the similarity. Examples of TH_(ROI)patterns include at least one of: a spatial pattern (e.g., a pattern ina thermal image received from a FPA sensor), a pattern in the timedomain (e.g., a pattern detected in a time series of the thermalmeasurements), and a pattern in the frequency domain (e.g., a patterndetected in a Fourier transform of the thermal measurements).

Biofeedback sessions may have different target states in differentembodiments. Generally, the purpose of a session is to bring the user'sstate during the biofeedback session (the “present state”) to becomemore similar to a target state. In one embodiment, while the user was inthe target state, one or more of the following were true: the user washealthier compared to the present state, the user was more relaxedcompared to the present state, a stress level of the user was below athreshold, and the user was more concentrated compared to the presentstate. Additionally, the computer may receive an indication of a periodduring which the user was in the target state based on a report made bythe user (the previous TH_(ROI) pattern comprises TH_(ROI) taken duringthe period), measurements of the user with a sensor other than CAM,semantic analysis of text written by the user, and/or analysis of theuser's speech.

In another embodiment, the computer calculates a value indicative ofsimilarity between current TH_(ROI) and previous TH_(ROI) of the usertaken while the user was in a target state, and generates the feedbackbased on the similarity. The similarity may be calculated by comparing(i) a current value of a characteristic of the user's breathing,calculated based on TH_(ROI), to (ii) a target value of thecharacteristic of the user's breathing, calculated based on the previousTH_(ROI). Here, the feedback may be indicative of whether the currentvalue of the characteristic of the user's breathing has become moresimilar or less similar to the target value of the characteristic of theuser's breathing since a previous (related) feedback.

In still another embodiment, the computer compares a current setcomprising feature values generated based on TH_(ROI) to a target setcomprising feature values generated based on previous TH_(ROI) of theuser, where the feature values are indicative of values of respiratoryparameter(s).

In some embodiments, the system configured to provide a breathingbiofeedback session receives indications of when the user is in thetarget state. Given such indications, the system may collect TH_(ROI)taken during these times and utilize them in biofeedback sessions tosteer the user towards the desired target (these collected TH_(ROI) maybe considered as the previous TH_(ROI) mentioned above). There arevarious sources for the indications of when the user is in the certaintarget state. In one example, the user may report when he/she is in sucha state (e.g., through an “app” or a comment made to a software agent).In another example, measurements of the user with one or more sensorsother than CAM may provide indications that the user is in a certainphysiological and/or emotional state that corresponds to the certaintarget state. In still another example, an indication of a period oftime in which the user was in a certain target state may be derived fromanalysis of communications of the user, such as using semantic analysisof text written by the user, and/or analysis of the user's speech.

In some embodiments, the computer may utilize a machine learning-basedmodel to determine whether the session is successful (or is expected tobe) and/or to determine the user's progress in the breathing biofeedbacksession at a given time (e.g., the rate of improvement the user isdisplaying at that time and/or how close the user is to the session'starget). Optionally, the computer generates feature values based onTH_(ROI) (e.g., values of TH_(ROI) and/or statistics of TH_(ROI) takenover different periods during the session), and utilizes the model tocalculate a value indicative of the progress and/or session success.Optionally, the model is trained on samples comprising feature valuesbased on previously taken TH_(ROI) and labels indicative of the successof the session and/or progress at the time those TH_(ROI) were taken.Optionally, the samples may be generated based on previously takenTH_(ROI) of the user. Additionally or alternatively, the samples may begenerated based on previously taken TH_(ROI) of other users. Optionally,the samples include samples generated based on TH_(ROI) taken ondifferent days, and/or while the measured user was in differentsituations.

The following method for providing a breathing biofeedback session maybe used, in some embodiments, by systems modeled according to FIG. 38.The steps described below may be performed by running a computer programhaving instructions for implementing the method. Optionally, theinstructions may be stored on a computer-readable medium, which mayoptionally be a non-transitory computer-readable medium. In response toexecution by a system including a processor and memory, the instructionscause the system to perform the following steps: In Step 1, takingthermal measurements of a region below the nostrils (TH_(ROI)) of a userusing at least one CAM worn on the user's head; TH_(ROI) are indicativeof the exhale stream. In Step 2, taking target TH_(ROI) (TARGET) whenthe user is in a desired state, such as relaxed, healthy, happy,energized, and/or in a state of elevated concentration. In Step 3,taking current TH_(ROI) (CURRENT) of the user. And in Step 4, providingthe user with real-time feedback indicative of similarity between TARGETand CURRENT.

Generating the feedback may involve various calculations in differentembodiments. For example, the method may include one or more of thefollowing steps: (i) calculating target smoothness of the exhale streambased on TARGET and calculating current smoothness of the exhale streambased on CURRENT. Optionally, the feedback is indicative of similaritybetween the target smoothness and the current smoothness, (ii)calculating target shape of the exhale stream (SHAPE) based on TARGETand calculating current SHAPE based on CURRENT. Optionally, the feedbackis indicative of similarity between the current SHAPE and the targetSHAPE, and/or (iii) calculating target breathing rate variability (BRV)based on TARGET and calculating current BRV based on CURRENT.Optionally, BRV is indicative of variations between consecutivebreathes, and the feedback is indicative of similarity between thecurrent BRV and the target BRV.

In one embodiment, a system configured to select a state of a userincludes at least one CAM and a computer. Each of the at least one CAMis worn on the user's head and takes thermal measurements of at leastthree regions below the nostrils (TH_(S)) of the user; wherein TH_(S)are indicative of shape of the exhale stream (SHAPE). The computer (i)generates feature values based on TH_(S), where the feature values areindicative of the SHAPE, and (ii) utilize a model to select the state ofthe user, from among potential states of the user, based on the featurevalues. Optionally, the model is utilized to calculate a value based onthe feature values. In one example, the calculated value is indicativeof which state the user is in, and the computer may calculateprobabilities that the user is in each of the potential states, andselect the state for which the probability is highest. In anotherexample, the calculated value is an output of a classifier (e.g., aneural network-based classifier), which is indicative of the state theuser is in.

In order for TH_(S) to be indicative of the SHAPE, the at least one CAMneeds to capture at least three regions from which the shape can beinferred. In a first example, the sensing elements of the at least oneCAM include: (i) at least three vertical sensing elements pointed atdifferent vertical positions below the nostrils where the exhale streamis expected to flow, and/or (ii) at least three horizontal sensingelements pointed at different horizontal positions below the nostrilswhere the exhale stream is expected to flow. Optionally, the larger thenumber of the vertical sensing elements that detect the exhale stream,the longer the length of the exhale stream, and the larger the number ofthe horizontal sensing elements that detect the exhale stream, the widerthe exhale stream. Additionally, the amplitude of the temperaturechanges measured by the sensing elements may also be used to estimatethe shape and/or uniformity of the exhale stream. It is noted that whena CAM, from among the at least one CAM, is located above the upper lipand pointed downwards, the vertical sensing elements (from the secondexample above) also provide data about the width of the exhale stream,and the horizontal sensing elements also provide data about the lengthof the exhale stream.

In a second example, the at least three regions from which the shape canbe inferred are located on (i) at least two vertical positions below thenostrils having a distance above 5 mm between their centers, and (ii) atleast two horizontal positions below the nostrils having a distanceabove 5 mm between their centers. Optionally, the at least three regionsrepresent: (i) parameters of a 3D shape that confines the exhale stream,and TH_(S) are the parameters' values, (ii) locations indicative ofdifferent lengths of the exhale stream (such as 8 cm, 16 cm, 24 cm, and32 cm), and/or (iii) locations indicative of different anglescharacteristic of directions of some of the different SHAPES of theexhale stream (such as locations indicative of a difference of as atleast 5°, 10°, or 25° between the directions of the different SHAPEs).

The potential states corresponding to the different SHAPEs may includevarious physiological and/or emotional states, and usually have to belearned and classified for each user because they depend on the user'sphysiological and emotional composition. Additionally, the potentialstates may include general states corresponding to either being healthyor being unhealthy. In some embodiments, at least some of the potentialstates may correspond to being in a state in which a certainphysiological response is likely to occur in the near future (e.g.,within the next thirty minutes). Thus, identifying that the user is insuch a state can be used to alert regarding the certain physiologicalresponse which the user is expected to have in order for the user and/orsome other party to take action to address it.

The feature values generated by the computer in order to calculate theSHAPE may include some of the various feature values described in thisdisclosure that are used to detect a physiological response. Inparticular, one or more of the feature values are generated based onTH_(S), and may include raw and/or processed values collected by one ormore sensing elements of the at least one CAM. Additionally oralternatively, these feature values may include feature values derivedfrom analysis of TH_(S) in order to determine various characteristics ofthe user's breathing. The feature values include at least one featurevalue indicative of the SHAPE. For example, the at least one featurevalue may describe properties of the thermal patterns of TH_(S).Optionally, the feature values include additional feature valuesindicative of the breathing rate, breathing rate variability, and/orsmoothness of the exhale stream.

The model used to select the user's state based on TH_(S) (andoptionally other sources of data) may be, in some embodiments, a machinelearning-based model. Optionally, the model is trained based on samplescomprising feature values generated based on previous on TH_(S) takenwhen the user being measured was in a known state. Optionally, theprevious TH_(S) include thermal measurements of one or more other users(who are not the user whose state is selected based on TH_(S)); in thiscase, the model may be considered a general model. Optionally, theprevious TH_(S) include thermal measurements of the user whose state isselected based on TH_(S); in this case, the model may be consideredpersonalized for this user. Optionally, the previous TH_(S) includethermal measurements taken during different days. Optionally, for eachstate from among the potential states, the samples include one or moresamples that are generated based on TH_(S) taken while the user beingmeasured was in the state. Optionally, the model was trained based on:previous TH_(S) taken while the user was in a first potential state fromamong the potential states, and other previous TH_(S) taken while theuser was in a second potential state from among the potential states.Optionally, the model was trained based on: previous TH_(S) taken fromusers while the users were in a first potential state from among thepotential states, and other previous TH_(S) taken while the users werein a second potential state from among the potential states. Optionally,for the same breathing rate, respiration volume, and dominant nostril,the computer is configured to select different states when TH_(S) areindicative of different SHAPEs that correspond to different potentialstates.

For each state from among the potential states, the samples include oneor more samples that have a label corresponding to the state. The labelsfor the samples may be generated based on indications that may come fromvarious sources. In one embodiment, a user whose TH_(S) are used togenerate a sample may provide indications about his/her state, such asby entering values via an app when having a headache or an anger attack.Additionally or alternatively, an observer of that user, which may beanother person or a software agent, may provide indications about theuser's state. For example, a parent may determine that certain behaviorpatterns of a child correspond to displaying symptomatic behavior of acertain state. In another embodiment, indications of the state of a userwhose TH_(S) are used to generate a sample may be determined based onmeasurements of physiological signals of the user, such as measurementsof the heart rate, heart rate variability, galvanic skin response,and/or brain activity (e.g., using EEG).

In some embodiments, characteristics of the user's breathing may beindicative of a future state of the user (e.g., a state to which theuser may be transitioning). Thus, certain changes in the characteristicsof the user's breathing can be used to predict the future state. Inthese cases, some samples that include feature values generated based onTH_(S) taken during a certain period may be assigned a label based on anindication corresponding to a future time (e.g., a label correspondingto the state of the user 15 or 30 minutes after the certain period). Amodel trained on such data may be used to predict the user's state atthe future time and/or calculate a value indicative of the probabilitythat the user will be in a certain state a certain amount of time intothe future.

Given a set of samples that includes feature values generated based onTH_(S) (and optionally the other sources of data) and labels indicativeof the state, the model can be trained using various machinelearning-based training algorithms. Optionally, the model may includevarious types of parameters, depending on the type of training algorithmutilized to generate the model. For example, the model may includeparameters of one or more of the following: a regression model, asupport vector machine, a neural network, a graphical model, a decisiontree, a random forest, and other models of other types of machinelearning classification and/or prediction approaches.

In some embodiments, a deep learning algorithm may be used to train themodel. In one example, the model may include parameters describingmultiple hidden layers of a neural network. In one embodiment, whenTH_(S) include measurements of multiple pixels, such as when the atleast one CAM includes a FPA, the model may include a convolution neuralnetwork (CNN). In one example, a CNN may be utilized to identify certainpatterns in the thermal images, such as patterns of temperatures in theregion of the exhale stream that may be indicative a respiratoryparameter, which involve aspects such as the location, direction, size,and/or shape of an exhale stream from the nose and/or mouth. In anotherexample, determining a state of the user based on one characteristics ofthe user's breathing (e.g., various respiratory parameters), may be donebased on multiple, possibly successive, thermal measurements.Optionally, estimating the state of the user may involve retaining stateinformation about the one or more characteristics that is based onprevious measurements. Optionally, the model may include parameters thatdescribe an architecture that supports such a capability. In oneexample, the model may include parameters of a recurrent neural network(RNN), which is a connectionist model that captures the dynamics ofsequences of samples via cycles in the network's nodes. This enablesRNNs to retain a state that can represent information from anarbitrarily long context window. In one example, the RNN may beimplemented using a long short-term memory (LSTM) architecture. Inanother example, the RNN may be implemented using a bidirectionalrecurrent neural network architecture (BRNN).

In order to generate a model suitable for identifying the state of theuser in real-world day-to-day situations, in some embodiments, thesamples used to train the model are based on thermal measurements (andoptionally the other sources of data) taken while the user was indifferent situations, locations, and/or conducting different activities.For example, the model may be trained based on some sample based onprevious thermal measurements taken while the user was indoors and othersamples based on other previous thermal measurements taken while theuser was outdoors. In another example, the model may be trained based onsome sample based on some previous thermal measurements taken while theuser was sitting and other samples based on other previous thermalmeasurements taken while the user was walking.

In one embodiment, the computer detects the SHAPE based on TH_(S).Optionally, the detected SHAPE corresponds to a certain state of theuser, and the computer bases the selection of the state on the detectedSHAPE. Optionally, the computer generates one or more of the featurevalues used to select the state based on the detected SHAPE. Forexample, the one or more feature values may be indicative of variousparameters of the SHAPE (e.g., parameters of a 3D geometrical body towhich the SHAPE corresponds).

To detect the SHAPE the computer may utilize a model that was trainedbased on previous TH_(S) of the user. Optionally, the previous TH_(S) ofthe user were taken during different days. In one embodiment, the modelincludes one or more reference patterns generated based on the previousTH_(S). Optionally, each reference pattern corresponds to a certainSHAPE, and is based on a subset of the previous TH_(S) for which thecertain SHAPE was identified. For example, identifying the certain SHAPEmay be done using analysis of thermal images of the exhale streamobtained using an external thermal camera that is not head-mountedand/or by a human expert. In this embodiment, detecting the SHAPE may bedone by comparing TH_(S) to the one or more reference thermal patternsand determining whether there is a sufficiently high similarity betweenthe thermal pattern of TH_(S) and at least one of the one or morereference thermal patterns.

In another embodiment, the model may be a machine learning-based modelthat was trained on samples, with each sample comprising feature valuesgenerated based on a subset of the previous TH_(S) (e.g., the subsetincludes previous TH_(S) taken during a certain period), and a labelrepresenting the SHAPE corresponding to the subset of the previousTH_(S). In one example, the feature values include values oftemperatures of various sensing elements of the at least one CAM. Inanother example, the feature values may include low-level imageproperties obtained by applying various image processing techniques tothe subset of the previous TH_(S). In this embodiment, detecting theSHAPE may be done by generating feature values based on TH_(S) andutilizing the model to calculate, based on the feature values, a valueindicative of the SHAPE corresponding TH_(S).

The SHAPE is a property that may be independent, at least to a certainextent, of other respiratory parameters. Thus, TH_(S) taken at differenttimes may have different SHAPEs detected, even if some other aspects ofthe breathing at those times are the same (as determined based on valuesof certain respiratory parameters). In one example, for the samebreathing rate of the user, the computer detects a first SHAPE based ona first TH_(S), and detects a second SHAPE based on a second TH_(S). Inthis example, the first and second TH_(S) have different thermalpatterns, e.g., as determined using a similarity function between vectorrepresentations of the first and second TH_(S) (which gives a similaritybelow a threshold). In another example, for the same breathing rate,respiration volume and dominant nostril, the computer detects a firstSHAPE based on a first TH_(S), and detects a second SHAPE based on asecond TH_(S) (where the first and second TH_(S) have different thermalpatterns).

In one embodiment, the system includes a frame worn on the user's head.Each of the at least one CAM is located less than 15 cm from the user'sface and does not occlude any of the user's mouth and nostrils. The atleast one CAM includes at least first and second inward-facinghead-mounted thermal cameras (CAM1 and CAM2, respectively) that takeTH_(ROI1) and TH_(ROI2), respectively. CAM1 is physically coupled to theright half of the frame and captures the exhale stream from the rightnostril better than it captures the exhale stream from the left nostril,and CAM2 is physically coupled to the left half of the frame andcaptures the exhale stream from the left nostril better than it capturesthe exhale stream from the right nostril. In another embodiment, the atleast three regions below the nostrils include a first region on theright side of the user's upper lip, a second region on the left side ofthe user's upper lip, and a third region on the mouth of the user, wherethermal measurements of the third region are indicative of the exhalestream from the user's mouth. In still another embodiment, the at leastthree regions below the nostrils include a first region comprising aportion of the volume of the air below the right nostril where theexhale stream from the right nostril flows, a second region comprising aportion of the volume of the air below the left nostril where the exhalestream from the left nostril flows, and a third region comprising aportion of a volume protruding out of the mouth where the exhale streamfrom the user's mouth flows.

In one embodiment, a system configured to present a user's state basedon SHAPE, includes a CAM and a UI. The at least one CAM takes thermalmeasurements of at least three regions below the nostrils (TH_(S)) ofthe user, where TH_(S) are indicative of SHAPE. The UI present theuser's state based on TH_(S). Optionally, for the same breathing rate,the UI presents different states for the user when TH_(S) are indicativeof different SHAPEs that correspond to different potential states.Optionally, each of the at least one CAM does not occlude any of theuser's mouth and nostrils. Optionally, the system further includes acomputer that generates feature values based on TH_(S), and utilizes amodel to select the state, from among potential states, based on thefeature values.

The following method for selecting a state of a user may be performed byrunning a computer program having instructions for implementing themethod. Optionally, the instructions may be stored on acomputer-readable medium, which may optionally be a non-transitorycomputer-readable medium. In response to execution by a system includinga processor and memory, the instructions cause the system to perform thefollowing steps: In Step 1, taking thermal measurements of at leastthree regions below the nostrils (TH_(S)) of the user utilizing aninward-facing head-mounted thermal camera (CAM); wherein TH_(S) areindicative of SHAPE. In Step 2, generating feature values based onTH_(S), where the feature values are indicative of the SHAPE. And inStep 3, utilizing a model for selecting the state of the user, fromamong potential states of the user, based on the feature values.

Optionally, the method further includes selecting different states, forthe same breathing rate, when TH_(S) are indicative of different SHAPEsthat correspond to different potential states. Optionally, the methodfurther includes training the model based on: previous TH_(S) takenwhile the user was in a first potential state from among the potentialstates, and other previous TH_(S) taken while the user was in a secondpotential state from among the potential states.

FIG. 39, FIG. 40, and FIG. 41 illustrate one embodiment of eyeglasses700 with head-mounted thermal cameras, which are able to differentiatebetween different states of the user based on thermal patterns of theforehead. The illustrated system includes first and second CAMs (701,702) mounted to the upper right and left portions of the eyeglassesframe, respectively, to take thermal measurements of the forehead. Thesystem further include a sensor 703 mounted to the bridge, which may beutilized to take measurements (m_(conf)) indicative of an occurrence ofone or more of the various confounding factors described herein. TheCAMs forward the thermal measurements to a computer that maydifferentiate, based on the thermal measurements of the forehead,between normal and abnormal states of a user (which are illustrated asnormal vs migraine vs angry in FIG. 39, and not angry vs angry in FIG.40). The computer may further differentiate between extents of acondition, which is illustrated as severe OCD vs less severe OCD after atreatment in FIG. 41.

In one embodiment, a system configured to differentiate between normaland abnormal states, includes at least one CAM and a computer. The atleast one CAM is worn on a user's head and takes thermal measurements ofat least first and second regions on the right side of the forehead(TH_(R1) and TH_(R2), respectively) of the user. The at least one CAMfurther takes thermal measurements of at least third and fourth regionson the left side of the forehead (TH_(L1) and TH_(L2), respectively).The middles of the first and third regions are at least 1 cm above themiddles of the second and fourth regions, respectively. Each of theleast one CAM is located below the first and third regions, and does notocclude any portion of the first and third regions. Optionally, CAM alsodoes not occlude the second and fourth regions. The computer determines,based on TH_(R1), TH_(R2), TH_(L1), and TH_(L2), whether the user is ina normal state or an abnormal state. Preferably, this embodiment assumesthat the user's hair does not occlude the first, second, third andfourth regions on the forehead. Optionally, the at least one CAMincludes a CAM that includes a sensor and a lens, and the sensor planeis tilted by more than 2° relative to the lens plane according to theScheimpflug principle in order to capture sharper images by the CAM,when at least one CAM is worn by the user. Here, the lens plane refersto a plane that is perpendicular to the optical axis of the lens, whichmay include one or more lenses.

In one embodiment, the at least one CAM includes at least first andsecond inward-facing head-mounted thermal cameras (CAM1 and CAM2,respectively) located to the right and to the left of the verticalsymmetry axis that divides the user's face, respectively (i.e., the axisthe goes down the center of the user's forehead and nose). CAM1 isconfigured to take TH_(R1) and TH_(R2), and CAM2 is configured to takeTH_(L1) and TH_(L2). Optionally, CAM1 and CAM2 are located at least 1 cmfrom each other. In one example, CAM1 and CAM2 are 701 and 702 that areillustrated in FIG. 40. Being able to detect a pattern on the foreheadmay involve utilization of multiple sensing elements (pixels) by each ofCAM1 and CAM2. Optionally, each of CAM1 and CAM2 weighs below 10 g, islocated less than 10 cm from the user's face, and includesmicrobolometer or thermopile sensor with at least 6 sensing elements.Optionally, CAM1 includes at least two multi-pixel thermal cameras, onefor taking measurements of the first region, and another one for takingmeasurements of the second region; CAM2 also includes at least twomulti-pixel thermal cameras, one for taking measurements of the thirdregion, and another one for taking measurements of the fourth region.

The computer determines, based on TH_(R1), TH_(R2), TH_(L1), andTH_(L2), whether the user is in a normal state or an abnormal state. Inone embodiment, the state of the user is determined by comparingTH_(R1), TH_(R2), TH_(L1), and TH_(L2) to reference thermal patterns ofthe forehead that include at least one reference thermal pattern thatcorresponds to the normal state and at least one reference thermalpattern that corresponds to the abnormal state. Optionally, a referencethermal pattern is determined from previous TH_(R1), TH_(R2), TH_(L1),and TH_(L2) of the user, taken while the user was in a certain statecorresponding to the reference thermal pattern (e.g., normal or abnormalstates). Determining whether TH_(R1), TH_(R2), TH_(L1), and TH_(L2), aresimilar to a reference thermal pattern may be done using various imagesimilarity functions, such as determining the distance between eachpixel in the reference thermal pattern and its counterpart in TH_(R1),TH_(R2), TH_(L1), or TH_(L2). One way this can be done is by convertingTH_(R1), TH_(R2), TH_(L1), or TH_(L2) into a vector of pixeltemperatures, and comparing it to a vector of the reference thermalpattern (using some form of vector similarity metric like a dot productor the L2 norm). Optionally, if the similarity reaches a threshold, theuser is considered to be in the state to which the reference thermalpattern corresponds.

In another embodiment, the computer determines that the user is in acertain state (e.g., normal or abnormal) by utilizing a model tocalculate, based on feature values generated from TH_(R1), TH_(R2),TH_(L1), and TH_(L2), a value indicative of the extent to which the useris in the certain state. Optionally, the model is trained based onsamples, each comprising feature values generated based on previousTH_(R1), TH_(R2), TH_(L1), and TH_(L2) of the user, taken while the userwas in the certain state. In some embodiments, determining whether theuser is in a certain state involves determining that TH_(R1), TH_(R2),TH_(L1), and TH_(L2) taken during at least a certain period of time(e.g., at least ten seconds, at least one minute, or at least tenminutes) are similar to a reference thermal pattern that corresponds tothe certain state.

Being in a normal/abnormal state may correspond to different behavioraland/or physiological responses. In one embodiment, the abnormal stateinvolves the user displaying symptoms of one or more of the following:an anger attack, Attention Deficit Disorder (ADD), and Attention DeficitHyperactivity Disorder (ADHD). In this embodiment, being in the normalstate refers to usual behavior of the user that does not involvedisplaying said symptoms. In another embodiment, when the user is in theabnormal state, the user will display within a predetermined duration(e.g., shorter than an hour), with a probability above a predeterminedthreshold, symptoms of one or more of the following: anger, ADD, andADHD. In this embodiment, when the user is in the normal state, the userwill display the symptoms within the predetermined duration with aprobability below the predetermined threshold. In yet anotherembodiment, when the user is in the abnormal state the user suffers froma headache, and when the user is in the normal state, the user does notsuffer from a headache. In still another embodiment, the abnormal staterefers to times in which the user has a higher level of concentrationcompared to the normal state that refers to time in which the user has ausual level of concentration. Although the thermal patterns of theforehead are usually specific to the user, they are usually repetitive,and thus the system may able to learn some thermal patterns of the userthat correspond to various states.

Touching the forehead can change the forehead's thermal pattern, eventhough the user's state did not actually change. Optionally, the systemfurther includes a sensor configured to provide an indication indicativeof whether the user touches the forehead. Although the touch is expectedto influence thermal readings from the touched area, the computer maycontinue to operate, for a predetermined duration, according to a stateidentified shortly (e.g., 1-20 sec) before receiving the indication,even if it identifies a different state shortly (e.g., less than 10, 20,30, or 60 sec) after receiving the indication. In one example, thesensor is a visible-light camera, and the computer uses image processingto determine whether the user touched the forehead and/or for how long.

The computer may alert the user responsive to identifying anirregularity in TH_(R1), TH_(R2), TH_(L1), and TH_(L2), which does notresult from interference, such as touching the forehead. For example,the irregularity may involve a previously unobserved thermal pattern ofthe forehead. Optionally, the user may be questioned in order todetermine if there is a medical reason for the irregularity, such as astroke or dehydration, in which case medical assistance may be offered,e.g., by summoning medical personnel to the user's location. Optionally,the computer alerts the user when identifying that the user is in anabnormal state associated with antisocial behavior (e.g., an angerattack).

Additional thermal cameras may be utilized to take thermal measurementsthat may be used to detect the user's state. For example, the system mayinclude at least one additional CAM for taking thermal measurements ofregions on the nose and below the nostrils (TH_(ROI3) and TH_(ROI4),respectively) of the user. Optionally, the additional CAM weighs below10 g, is physically coupled to a frame worn on the user's head, and islocated less than 15 cm from the face. Optionally, the computerdetermines the user's state also based on TH_(ROI3) and TH_(ROI4).Optionally, the computer (i) generates feature values based on TH_(R1),TH_(R2), TH_(L1), TH_(L2), TH_(ROI1), and TH_(ROI4), and (ii) utilizes amodel to determine the user's state based on the feature values.Optionally, the model was trained based on a first set of previousTH_(R1), TH_(R2), TH_(L1), TH_(L2), TH_(ROI3), and TH_(ROI4) taken whilethe user was in the normal state and a second set of previous TH_(R1),TH_(R2), TH_(L1), TH_(L2), TH_(ROI3), and TH_(ROI4) taken while the userwas in the abnormal state.

In another example, the system may include another CAM for takingthermal measurements of a region on the periorbital area (TH_(ROI3)) ofthe user. Optionally, the computer determines the state of the user alsobased on TH_(ROI3). Optionally, the computer is further configured to:(i) generate feature values based on TH_(R1), TH_(R2), TH_(L1), TH_(L2),and TH_(ROI3), and (ii) utilize a model to determine the user's statebased on the feature values. Optionally, the model was trained based ona first set of previous TH_(R1), TH_(R2), TH_(L1), TH_(L2), andTH_(ROI3) taken while the user was in the normal state and a second setof previous TH_(R1), TH_(R2), TH_(L1), TH_(L2), and TH_(ROI3) takenwhile the user was in the abnormal state.

Determining the user's state based on TH_(R1), TH_(R2), TH_(L1), andTH_(L2) (and optionally other sources of data) may be done using amachine learning-based model. Optionally, the model is trained based onsamples comprising feature values generated based on previous TH_(R1),TH_(R2), TH_(L1), and TH_(L2) taken when the user was in a known state(e.g., for different times it was known whether the user was in thenormal or abnormal state). Optionally, the user may provide indicationsabout his/her state, such as by entering values via an app when having aheadache or an anger attack. Additionally or alternatively, an observerof the user, which may be another person or a software agent, mayprovide the indications about the user's state. For example, a parentmay determine that certain behavior patterns of a child correspond todisplaying symptomatic behavior of ADHD. In another example, indicationsof the state of the user may be determined based on measurements ofphysiological signals of the user, such as measurements of the heartrate, heart rate variability, breathing rate, galvanic skin response,and/or brain activity (e.g., using EEG).

In some embodiments, one or more of the feature values in the samplesmay be based on other sources of data (different from TH_(R1), TH_(R2),TH_(L1), and TH_(L2)). These may include additional thermal cameras,additional physiological measurements of the user, and/or measurementsof the environment in which the user was while the measurements weretaken. In one example, at least some of the feature values used insamples include additional physiological measurements indicative of oneor more of the following signals of the user: heart rate, heart ratevariability, brainwave activity, galvanic skin response, muscleactivity, and extent of movement. In another example, at least some ofthe feature values used in samples include measurements of theenvironment that are indicative of one or more of the following valuesof the environment in which the user was in: temperature, humiditylevel, noise level, air quality, wind speed, and infrared radiationlevel.

Given a set of samples comprising feature values generated based onTH_(R1), TH_(R2), TH_(L1), and TH_(L2) (and optionally the other sourcesof data) and labels generated based on the indications, the model can betrained using various machine learning-based training algorithms.Optionally, the model is utilized by a classifier that classifies theuser's state (e.g., normal/abnormal) based on feature values generatedbased on TH_(R1), TH_(R2), TH_(L1), and TH_(L2) (and optionally theother sources). Optionally, the model may include various types ofparameters, depending on the type of training algorithm utilized togenerate the model. For example, the model may include parameters of oneor more of the following: a regression model, a support vector machine,a neural network, a graphical model, a decision tree, a random forest,and other models of other types of machine learning classificationand/or prediction approaches.

In some embodiments, the model is trained utilizing deep learningalgorithms. Optionally, the model includes parameters describingmultiple hidden layers of a neural network. Optionally, the modelincludes a convolution neural network (CNN), which is useful foridentifying certain patterns in the thermal images, such as patterns oftemperatures on the forehead. Optionally, the model may be utilized toidentify a progression of a state of the user (e.g., a gradual formingof a certain thermal pattern on the forehead). In such cases, the modelmay include parameters that describe an architecture that supports acapability of retaining state information. In one example, the model mayinclude parameters of a recurrent neural network (RNN), which is aconnectionist model that captures the dynamics of sequences of samplesvia cycles in the network's nodes. This enables RNNs to retain a statethat can represent information from an arbitrarily long context window.In one example, the RNN may be implemented using a long short-termmemory (LSTM) architecture. In another example, the RNN may beimplemented using a bidirectional recurrent neural network architecture(BRNN).

In order to generate a model suitable for identifying the state of theuser in real-world day-to-day situations, in some embodiments, thesamples used to train the model are based on thermal measurements (andoptionally the other sources of data) taken while the user was indifferent situations, locations, and/or conducting different activities.In a first example, the model may be trained based on a first set ofprevious thermal measurements taken while the user was indoors and inthe normal state, a second set of previous thermal measurements takenwhile the user was indoors and in the abnormal state, a third set ofprevious thermal measurements taken while the user was outdoors and inthe normal state, and a fourth set of previous thermal measurementstaken while the user was outdoors and in the abnormal state. In a secondexample, the model may be trained based on a first set of previousthermal measurements taken while the user was sitting and in the normalstate, a second set of previous thermal measurements taken while theuser was sitting and in the abnormal state, a third set of previousthermal measurements taken while the user was standing and/or movingaround and in the normal state, and a fourth set of previous thermalmeasurements taken while the user was standing and/or moving around andin the abnormal state. Usually the movements while standing and/ormoving around, and especially when walking or running, are greatercompared to the movement while sitting; therefore, a model trained onsamples taken during both sitting and standing and/or moving around isexpected to perform better compared to a model trained on samples takenonly while sitting.

Having the ability to determine the state of the user can beadvantageous when it comes to scheduling tasks for the user and/ormaking recommendations for the user, which suits the user's state. Inone embodiment, responsive to determining that the user is in the normalstate, the computer prioritizes a first activity over a second activity,and responsive to determining that the user is in the abnormal state,the computer prioritizes the second activity over the first activity.Optionally, accomplishing each of the first and second activitiesrequires at least a minute of the user's attention, and the secondactivity is more suitable for the abnormal state than the firstactivity. Optionally, and the first activity is more suitable for thenormal state than the second activity. Optionally, prioritizing thefirst and second activities is performed by a calendar managementprogram, a project management program, and/or a “to do” list program.Optionally, prioritizing a certain activity over another means one ormore of the following: suggesting the certain activity before suggestingthe other activity, suggesting the certain activity more frequently thanthe other activity (in the context of the specific state), allottingmore time for the certain activity than for the other activity, andgiving a more prominent reminder for the certain activity than for theother activity (e.g., an auditory indication vs. a mention in a calendarprogram that is visible only if the calendar program is opened).

Such state-dependent prioritization may be implemented in variousscenarios. In one example, the normal state refers to a normalconcentration level, the abnormal state refers to a lower than normalconcentration level, and the first activity requires a high attentionlevel from the user compared to the second activity. For instance, thefirst and second activities may relate to different topics of aself-learning program for school; when identifying that the user is inthe normal concentration state, a math class is prioritized higher thana sports lesson; and when identifying that the user is in the lowerconcentration state, the math class is prioritized lower than the sportslesson. In another example, the normal state refers to a normal angerlevel, the abnormal state refers to a higher than normal anger level,and the first activity involves more interactions of the user with otherhumans compared to the second activity. In still another example, thenormal state refers to a normal fear level, the abnormal state refers toa panic attack, and the second activity is expected to have a morerelaxing effect on the user compared to the first activity.

In one embodiment, a system configured to alert about an abnormal stateincludes at least one CAM and a user interface (UI). The at least oneCAM takes thermal measurements of at least first and second regions onthe right side of the forehead (TH_(R1) and TH_(R2), respectively) ofthe user, and takes thermal measurements of at least third and fourthregions on the left side of the forehead (TH_(L1) and TH_(L2),respectively). The middles of the first and third regions are at least 1cm above the middles of the second and fourth regions, respectively.Each of the at least one CAM is located below the first and thirdregions, and does not occlude any portion of the first and thirdregions. The UI provides an alert about an abnormal state of the user,where the abnormal state is determined based on TH_(R1), TH_(R2),TH_(L1), and TH_(L2). Optionally, the system includes a transmitter thatmay be used to transmit TH_(R1), TH_(R2), TH_(L1), and TH_(L2) to acomputer that determines, based on TH_(R1), TH_(R2), TH_(L1), andTH_(L2), whether the user is in the normal state or the abnormal state.The computer may include a wearable computer, a computer belonging to asmartphone or a smartwatch carried by the user, and/or cloud-basedserver. Optionally, responsive to determining that the user is in anabnormal state, the computer commands the UI to provide the alert. Forexample, the computer may send a signal to a smartphone app, and/or to asoftware agent that has control of the UI, to provide the alert. Inanother example, the computer may send an instruction to the UI toprovide the alert. Optionally, the alert is provided as text, image,sound, and/or haptic feedback.

The following method for alerting about an abnormal state may be used,in some embodiments, by the system configured to alert about theabnormal state (described above). The steps described below may beperformed by running a computer program having instructions forimplementing the method. Optionally, the instructions may be stored on acomputer-readable medium, which may optionally be a non-transitorycomputer-readable medium. In response to execution by a system includinga processor and memory, the instructions cause the system to perform thefollowing steps:

In Step 1, taking thermal measurements of at least first and secondregions on the right side of the forehead (TH_(R1), TH_(R2)) of a user,and thermal measurements of at least third and fourth regions on theleft side of the forehead (TH_(L1), TH_(L2)) of the user. The middles ofthe first and third regions are at least 1 cm above the middles of thesecond and fourth regions, respectively.

In Step 2, generating feature values based on TH_(R1), TH_(R2), TH_(L1),and TH_(L2).

In Step 3, utilizing a model for detecting a state of the user based onthe feature values. The model was trained based on (i) previous featurevalues taken while the user was in a normal state, and (ii) otherprevious feature values taken while the user was in the abnormal state.

And in Step 4, responsive to detecting the abnormal state in Step 3,alerting about the abnormal state. In one example, the alerting mayinvolve providing text, image, sound, and/or haptic feedback via theuser interface.

Neurofeedback sessions can assist in treating various brainfunction-related conditions and/or disorders. In order to maximize theireffectives, it may be advantageous to have neurofeedback treatmentswhile a person suffers and/or exhibits the symptoms of a brainfunction-related condition and/or disorder. The following aredescriptions of embodiments of a wearable system that may be utilizedfor this purpose. Some embodiments of a neurofeedback system describedbelow involve a wearable, lightweight device that is aestheticallyacceptable, and may be utilized as needed in day-to-day situations.

Some examples of disorders that may be treated with some embodiments ofthe neurofeedback system described herein include disorders related to(i) frontal lobe dysfunction, such as ADHD, headaches, anger, anxiety,and depression, (ii) paroxysmal disorders, such as headaches, seizures,rage reactions, and panic attacks, (iii) chronic pain, and (iv) stress.It is noted that the term “neurofeedback” also covers biofeedback andother similar feedback-based treatments.

FIG. 38 (already discussed above) illustrates one embodiment of a systemconfigured to provide neurofeedback (based on measurements of CAM 720)and/or breathing biofeedback (based on measurements of at least some ofthermal cameras 723, 725, 727 and 729). The system illustrated in FIG.41, which uses two inward-facing head-mounted thermal cameras (701 and702) to measure the forehead, may be used for neurofeedback togetherwith a UI (not illustrated). Other embodiments of neurofeedback HMSs mayinvolve more than two inward-facing head-mounted thermal cameras tomeasure the forehead. Some embodiments of neurofeedback HMSs may includeone or more sensors such as the sensor 722, which are used to takem_(conf) as discussed below.

FIG. 43 illustrates a scenario in which a user has neurofeedback sessionduring a day-to-day activity, such as during school time. For example,the session may be initiated because the user felt that he was losingconcentration and/or the system might have determined that the user wasexhibiting symptoms of ADHD and/or was in an undesirable state. The userwears an Augmented Reality device (AR), which includes user interface710 that includes a display to present the augmented images. The ARincludes one or more inward-facing head-mounted thermal cameras, whichmay be similar to the system illustrated in FIG. 38. The neurofeedbacksession involves attempting to control brain activity by causing theaugmented reality video of the car 711 to drive forwards. For example,the car 711 drives forwards when the temperature at certain regions ofthe forehead 712 increases, and drives backwards when the temperature atthe certain regions of the forehead 712 decreases.

In one embodiment, a neurofeedback system includes at least aninward-facing head-mounted thermal camera (CAM) and a user interface(UI). Optionally, the neurofeedback system may include additionalelements such as a frame, a computer, and/or additional sensors and/orthermal cameras, as described below.

CAM is worn on a user's head and takes thermal measurements of a regionon the forehead (TH_(F)) of the user. CAM is positioned such that whenthe user is upright, CAM is located below the middle of the region onthe user's forehead. Optionally, CAM does not occlude the center of theforehead, and as such, may be more aesthetically pleasing than systemsthat have elements that occlude the center of the forehead. Optionally,CAM is located close to the forehead, at a distance below 15 cm, 10 cm,or 5 cm from the user's face. Optionally, CAM may use a single pixelsensor (e.g., discrete thermophile sensor) or a multiple pixel sensor(e.g., microbolometer FPA).

In one embodiment, TH_(F) measured by CAM includes the area known in thefield of electroencephalography as the “Fpz point”, which is typicallylocated at a point that is between 5% and 15% the distance from thenasion to the Inion (e.g., approximately at around 10% the distance).Optionally, in this embodiment, TH_(F) may be indicative of temperaturechanges at the Fpz point. Additionally or alternatively, the region onthe forehead measured by CAM may include the center of the forehead, andTH_(F) may optionally be indicative of temperature changes at the centerof the forehead.

In another embodiment, CAM may measure at least four areas on the user'sforehead covering regions on the upper right side of the forehead, lowerright side of the forehead, upper left side of the forehead, and lowerleft side of the forehead, respectively. Optionally, in this embodiment,TH_(F) may be indicative of a thermal pattern of the user's forehead.Optionally, in this embodiment, “CAM” refers to multiple inward-facingthermal cameras, which include at least first and second inward-facinghead-mounted thermal cameras (CAM1 and CAM2, respectively). CAM1 takesthe measurements of the upper right side of the forehead and the lowerright side of the forehead, and CAM2 takes the measurements of the upperleft side of the forehead and the lower left side of the forehead.Optionally, TH_(F) may include measurements of at least six areas on theuser's forehead. Optionally, the at least four areas and the at leastsix areas each include at least one area that covers the Fpz point.

Due to the proximity of CAM to the face, in some embodiments, there maybe an acute angle between the optical axis of CAM and the forehead. Inorder to improve the sharpness of thermal images of the forehead, insome embodiments, CAM may include a sensor and a lens, which areconfigured such that the sensor plane is tilted by more than 2° relativeto the lens plane according to the Scheimpflug principle, which mayenable the capture of sharper images of the forehead when CAM is closeto the face.

The UI provides a feedback to the user during the neurofeedback session,which is determined based on TH_(F) and optionally m_(conf) (that isindicative of confounding factors). Optionally, providing the sessionfor the user involves receiving instructions from the user (e.g., verbalcommands and/or menu selections), which may affect the type of feedbackthe user receives (e.g., what type of session or “game” will be playedin the session, how long the session should last, etc.).

In some embodiments, at least some of the feedback presented to the uservia the UI is intended to indicate to the user whether, and optionallyto what extent, the user's brain activity (as determined based onTH_(F)) is progressing towards a target. Optionally, the target maycorrespond to a state of brain activity that causes TH_(F) to have acertain value. Optionally, the target may correspond to a typical TH_(F)pattern of the user. Optionally, typical TH_(F) pattern of the user is apattern of temperatures on different points on the forehead, whichdetermined based on previous TH_(F) that are measured when the user wasin a typical, normal state, and not exhibiting symptoms of anger, ADHD,a headache, etc. In one example, the user may be considered to makeprogress in the neurofeedback session if the temperature of the forehead(or a certain region on the forehead) becomes closer to a targettemperature. In another example, the user may be considered to makeprogress in the neurofeedback session if the variability of temperaturesacross certain regions of the forehead reduces. In yet another example,the user may be considered to make progress in the neurofeedback sessionif asymmetry of temperatures of the forehead reduces. And in stillanother example, the user may be considered to make progress in theneurofeedback session if TH_(F) pattern measured during the sessionbecomes more similar to a certain target thermal pattern. Optionally,the user may receive feedback indicative of decreasing positive progress(or negative progress) when the TH_(F) pattern measured during thesession becomes less similar to the typical TH_(F) pattern.

In one embodiment, video played as part of the feedback is playedaccording to a protocol suitable for a passive infraredhemoencephalography (pIR BEG) session, which is a form of biofeedbackfor the brain that measures and displays information on the thermaloutput of the frontal lobe. In one configuration, pIR HEG involvesincreasing the forehead temperature by watching a movie that providesthe feedback. The movie plays when the measured forehead temperaturerises and stops when the temperature drops. The system may increase thethreshold as the user learns how to raise the forehead temperature, andthe user is instructed to calmly concentrate on making the moviecontinue to play.

The computer controls the neurofeedback session based on TH_(F) andoptionally m_(conf). In one embodiment, the computer compares TH_(F) toa target temperature. Optionally, different pixels of CAM may becompared to different target temperatures, or the target temperature mayrefer to an average temperature of the forehead. In another embodiment,the computer may calculate changes to temperature of the forehead(ΔT_(F)) based on TH_(F), and utilizes ΔT_(F) to control theneurofeedback session. In yet another embodiment, the computer maycompare TH_(F) to a target thermal pattern of the forehead, and theprogress of the user in the neurofeedback session is evaluated based ona similarity between TH_(F) and the target thermal pattern, and/or achange in extent of similarity between TH_(F) and the target thermalpattern.

In one embodiment, TH_(F) includes measurements of at least fournon-collinear regions on the forehead (e.g., all the four regions do notlie on the same straight line), and the computer controls theneurofeedback session by providing the user a feedback via the userinterface. The computer calculates a value indicative of similaritybetween a current TH_(F) pattern and a previous TH_(F) pattern of theuser taken while the user was in a target state, and generates, based onthe similarity, the feedback provided to the user as part of theneurofeedback session. The TH_(F) pattern may refer to a spatial patternof the at least four non-collinear regions on the forehead (e.g., apattern in a thermal image received from a FPA sensor), and/or to apattern in the time domain of the at least four non-collinear regions onthe forehead (e.g., a pattern detected in a time series of the thermalmeasurements).

Neurofeedback sessions may have different target states in differentembodiments. Generally, the purpose of a session is to bring the user'sstate during the neurofeedback session (the “present state”) to becomemore similar to a target state. In one embodiment, while the user was inthe target state, one or more of the following were true: the user washealthier compared to the present state, the user was more relaxedcompared to the present state, a stress level of the user was below athreshold, the user's pain level was below a threshold, the user had noheadache, the user did not suffer from depression, and the user was moreconcentrated compared to the present state. Additionally, the computermay receive an indication of a period during which the user was in thetarget state based on a report made by the user (the previous TH_(F)pattern comprises TH_(F) taken during the period), measurements of theuser with a sensor other than CAM, semantic analysis of text written bythe user, and/or analysis of the user's speech.

In some embodiments, the computer may utilize a machine learning-basedmodel to determine whether the session is successful (or is expected tobe) and/or to determine the user's progress in the neurofeedback sessionat a given time. Optionally, the computer generates feature values basedon TH_(F), and utilizes the model to calculate a value indicative of theprogress and/or session success. Optionally, the model is trained onsamples comprising feature values based on previously taken TH_(F) andlabels indicative of the success of the session and/or progress at thetime those TH_(F) were taken. Optionally, the samples may be generatedbased on previously taken TH_(F) of the user and/or of other users.Optionally, the samples include samples generated based on TH_(F) of theuser taken on different days, and/or while the user was in differentsituations.

At a given time, temperatures measured at different areas of theforehead may be different. A value, which is a function of thetemperatures at the different areas and is indicative of theirvariability, may be referred to herein as the “temperature variability”of the measurements. In one example, the function of the temperatures isthe statistical variance of the temperatures. Having high temperaturevariability can be a sign that the user is suffering from variousconditions, such as anger, a headache, depression, and/or anxiety.Optionally, a target of the neurofeedback session may be to lower thetemperature variability of TH_(F). Optionally, progress of theneurofeedback session may be evaluated based on a value of thetemperature variability of TH_(F), an extent that the temperaturevariability of TH_(F) has decreased, and/or a rate at which thetemperature variability of TH_(F) has decreased.

Various brain function-related conditions may be manifested viaasymmetrical thermal patterns on the forehead. Optionally, a target of aneurofeedback session in such cases may be to decrease the asymmetry ofthe thermal patterns. In one embodiment, CAM is located to the right ofthe vertical symmetry axis that divides the user's face (e.g. 701), andthe region is on the right side of the forehead. The neurofeedbacksystem may include a second inward-facing head-mounted thermal camera(e.g. 702), located to the left of the vertical symmetry axis, whichtakes thermal measurements of a second region on the left side of theforehead (TH_(F2)). Optionally, the computer provides to the user afeedback that becomes more positive as the temperature asymmetry betweenTH_(F) and TH_(F2) decreases.

Different regions on the forehead may be associated with differentimportance, with respect to various physiological responses and/orconditions that may be treated with neurofeedback sessions. In oneembodiment, regions that are more important are associated with higherweights compared to weights associated with regions that are lessimportant. Optionally, these weights may be utilized by the computer tocalculate various values such as an average temperature of the forehead,which with the weights may be considered a “weighted averagetemperature”. Similarly, a temperature variability of TH_(F) that iscalculated while taking into account the weights associated with thevarious areas may be a “weighted temperature variability”, andtemperature asymmetry between TH_(F) and TH_(F2), which is calculatedwhile taking into account the weights associated with the various areasmay be a “weighted temperature asymmetry”. In some embodiments,providing feedback to the user based on one or more of the above“weighted” values may increase the efficacy of the neurofeedbacksession.

The temperature variability may be an indicator for the success orfailure of the neurofeedback session. A session that causes a decreasingof the temperature variability below a certain first threshold may beconsidered a successful session that can be terminated, while a sessionthat causes an increase of the temperature variability above a certainsecond threshold may be considered a failed session that should beterminated in order to prevent worsening the symptoms. In oneembodiment, the computer terminates the neurofeedback session whenTH_(F) are indicative of the temperature variability decreasing belowthe certain first threshold. Additionally or alternatively, the computermay terminate the neurofeedback session when TH_(F) are indicative ofthe temperature variability increasing above the certain secondthreshold.

In a similar fashion, the temperature asymmetry may be an indicator forthe success or failure of the neurofeedback session for certaindisorders. In one embodiment, the computer terminates the neurofeedbacksession when TH_(F) are indicative of the temperature asymmetrydecreasing below a certain first threshold. Additionally oralternatively, the computer may terminate the neurofeedback session whenTH_(F) are indicative of the temperature asymmetry increasing above acertain second threshold.

Having neurofeedback sessions, in a real world, day-to-day situationscan involve conditions that are less sterile and not as controlled asthe conditions that typically encountered when conducting such sessionsat a clinic or a laboratory. In particular, thermal measurements of theforehead may be affected by various factors that are unrelated to thetype of brain activity the user is conducting, as part of the session;these factors may often be absent and/or less extreme in controlledsettings and/or may be noticed and accounted for by a practitioner (whofor example, may tell the user not to touch the forehead). Such factorsmay be referred to herein as confounding factors. Some examples ofconfounding factors include touching the forehead (e.g., with one'sfingers), thermal radiation directed at the forehead (e.g., directsunlight), and direct airflow on the forehead (e.g., from an airconditioner). Each of these factors can cause changes in TH_(F) that arenot due to brain activity. In order to account for one or more of theseconfounding factors, in some embodiments, the neurofeedback includes awearable sensor that takes measurements (denoted m_(conf)) indicative ofat least one of the following confounding factors: touching theforehead, thermal radiation directed at the forehead, and direct airflowon the forehead. Optionally, the wearable sensor is coupled to a frameworn on the user's head. The following are some examples of types ofsensors that the wearable sensor may involve in some embodiments of theneurofeedback system.

In one embodiment, the wearable sensor is an outward-facing head-mountedthermal camera (CAM_(out)) that takes thermal measurements of theenvironment (TH_(ENV)). Optionally, the angle between the optical axesof CAM and CAM_(out) is at least one or more of the following angles:45°, 90°, 130°, 170°, and 180°. In another embodiment, the wearablesensor provides measurements indicative of times at which the usertouches the forehead. Optionally, the wearable sensor includes avisible-light camera, a miniature radar (such as low-power radaroperating in the range between 30 GHz and 3,000 GHz), an activeelectro-optics distance measurement device (such as a miniature Lidar),and/or an ultrasound sensor. In yet another embodiment, the sensor maybe an anemometer that is physically coupled to a frame worn on theuser's head, is located less than 15 cm from the face, and provides avalue indicative of a speed of air directed at the face.

There are various way in which the computer may utilize m_(conf) toaccount for occurrences of a confounding factor during the neurofeedbacksession. In one embodiment, an occurrence of the confounding factor mayprompt the computer to alert the user about the occurrence. In oneexample, the computer may identify, based on m_(conf), that the extentof a confounding factor reached a threshold, and command the userinterface to alert the user that the neurofeedback session is lessaccurate due to the confounding factor. In another example, uponidentifying that the extent of a confounding factor reached thethreshold, the computer may refrain from updating the feedback providedto the user as part of the neurofeedback session for at least a certainduration. The certain duration may be a fixed period (e.g., 0.2 secondsfrom reaching the threshold), and/or may last until m_(conf) indicatethat the extent of the confounding factor is below the threshold.

In one embodiment, the computer may adjust the values of TH_(F) based onthe values of m_(conf) according to a certain function and/ortransformation. For example, TH_(F) may be normalized with respect tothe intensity of thermal radiation directed at the face and/or the speedof wind directed at the face. In another embodiment, in which thecomputer utilizes the machine learning-based model to calculate a valueindicative of the progress and/or success of the session, the computermay utilize m_(conf) to generate at least some of the feature valuesthat are utilized to calculate the value indicative of the progressand/or success. Optionally, the model is trained based on samples thatinclude at least some samples that are based on TH_(F) and m_(conf) thatwere taken while a confounding factor affected TH_(F).

Another approach that may be utilized by the computer, in someembodiments, is to learn to differentiate between changes to TH_(F) dueto brain activity and changes to TH_(F) due to various confoundingfactors (which may have different characteristics). In one embodiment,the computer may generate feature values based on sets of TH_(F) andm_(conf), and utilize a second machine learning-based model to detect,based on the feature values, whether a change in TH_(F) occurredresponsive to brain activity or a confounding factor. Optionally, thesecond model may be trained on samples generated based on measurementstaken at times that a confounding factor affected TH_(F) and on othersamples based on measurements taken at times that the confounding factordid not affect TH_(F).

It is to be noted that since in real-world scenarios confounding factorscan affect TH_(F), utilizing one or more of the various measuresdescribed above may assist the computer to provide better neurofeedbacksessions. Thus, in some embodiments, on average, neurofeedback sessionsbased on TH_(F) and m_(conf) provide better results than neurofeedbacksessions based on TH_(F) without m_(conf).

In addition to confounding factors of which m_(conf) may be indicative,in some embodiments, the computer may take into account in a similarway, other cofounding factors. In one embodiment, the neurofeedbacksystem may include an additional wearable and/or head-mounted sensorused to detect a movement of the frame relative to the head while theframe is still worn, a change in the user's position, and/or a change inthe user's body temperature. In another embodiment, the neurofeedbacksystem may include a humidity sensor and/or an environmental temperaturesensor, which may be coupled to the user.

Consumption of various substances may also be considered confoundingfactors. In one embodiment, the computer may receive an indication ofwhether the user took medication before the neurofeedback session (e.g.,the type of medication and dosage), whether the user smoked, consumedalcohol, etc. Each of these factors may affect TH_(F) in certain waysthat may not necessarily be because of the user's brain activity. In asimilar way to how the computer handles confounding factors in thedescription above, the computer may warn about the session beingineffective (e.g., after consuming alcohol or drugs) and/or performvarious normalizations and/or computations to address these confoundingfactors (e.g., by generating feature values indicating the consumptionof the substances).

Another way in which some confounding factors may be addressed, involvesproviding better insolation for the forehead region from the environmentwhile the neurofeedback session is being conducted. To this end, oneembodiment involves utilization of a clip-on structure designed to beattached and detached from the frame multiple times (e.g., it may beattached before a neurofeedback session starts and detached after thesession terminates). Optionally, the clip-on includes a cover thatoccludes (when attached to the frame) the forehead region measured byCAM, which drives the neurofeedback. The clip-on may protect the regionagainst environmental radiation, wind, and touching the region. FIG. 42illustrates a clip-on 716 configured to be attached and detached fromthe frame 700 multiple times. The clip-on 716 includes a coverconfigured to occlude the region on the user's forehead (when theclip-on is attached to the frame) and a mechanism that holds the clip-onto the frame.

This selective use of the clip-on 716 can enable CAM 718 to providedifferent types of measurements. For example, TH_(F) taken while theclip-on is attached may be less noisy then measurements taken when theclip-on is not attached. In some embodiments, measurements obtainedwithout the clip-on may be too noisy for an effective neurofeedbacksession due to environmental confounding factors. Thus, in oneembodiment, CAM may be used to detect that the user needs aneurofeedback session while the clip-on does not cover the region on theforehead (e.g., based on a thermal pattern of the forehead thatindicates that the user is in an abnormal state). Optionally, the useris prompted to attach the clip-on and commence with the neurofeedbacksession. After the clip-on is attached, CAM takes TH_(F) that are usedeffectively for the neurofeedback session (and may be of better qualitythan TH_(F) taken when the clip-on is not attached).

The neurofeedback system may include, in some embodiments, one or moreadditional CAMs to measure physiological signals indicative ofrespiration, stress, and other relevant parameters. Optionally, a targetof the neurofeedback session may include bringing these physiologicalsignals to a certain value in addition to a target that is related toTH_(F).

In one example, the neurofeedback system may include a secondinward-facing head-mounted thermal camera (CAM2) that takes thermalmeasurements of a region below the nostrils (TH_(N)), which isindicative of the user's breathing. Optionally, the computer may controlthe neurofeedback session also based on TH_(N). Optionally, TH_(N) isutilized to calculate values of one or more respiratory parameters, suchas breathing rate, exhale duration, and/or smoothness of the exhalestream. Optionally, a target state for the neurofeedback sessioninvolves having certain values of the one or more respiratory parametersfall in certain ranges. In one example, CAM2 may be the thermal camera727 or the thermal camera 729, which are illustrated in FIG. 38.Optionally, the computer calculates the user's breathing rate based onTH_(N), and guides the user to breathe at his/her resonant frequency,which maximizes the amplitude of respiratory sinus arrhythmia and is inthe range of 4.5 to 7.0 breaths/min.

In another example, the neurofeedback system may include second andthird inward-facing head-mounted thermal cameras (CAM2 and CAM3,respectively), which take thermal measurements of regions on theperiorbital area and the nose (TH_(ROI2) and TH_(ROI3), respectively).Optionally, the computer may control the neurofeedback session alsobased on TH_(ROI2) and TH_(ROI3). For example, the computer maycalculate a stress level of the user based on TH_(ROI2) and/orTH_(ROI3), and a target state of the neurofeedback session maycorrespond to a certain stress level the user is supposed to have.Optionally, TH_(ROI2) and TH_(ROI3) may be utilized to calculate astress level of the user. For example, CAM2 may be the thermal camera724 or 726, and CAM3 may be the thermal camera 733, which areillustrated in FIG. 38.

The following method for conducting a neurofeedback session may be used,in some embodiments, by systems modeled according to FIG. 38. The stepsdescribed below may be performed by running a computer program havinginstructions for implementing the method. Optionally, the instructionsmay be stored on a computer-readable medium, which may optionally be anon-transitory computer-readable medium. In response to execution by asystem including a processor and memory, the instructions cause thesystem to perform the following steps:

In Step 1, taking thermal measurements of a region on a forehead(TH_(F)) of the user, from a location below the middle of the region onthe user's forehead and less than 10 cm from the user's forehead.

In Step 2, taking measurements (m_(conf)) indicative of at least one ofthe following confounding factors: touching the forehead, thermalradiation directed at the forehead, and direct airflow on the forehead.

And in Step 3, conducting a neurofeedback session for the user based onTH_(F) and m_(conf). Optionally, the neurofeedback session is controlledby a computer, as described above. Optionally, the method furtherincludes generating feature values based on TH_(F) and m_(conf), andutilizing a model to control the neurofeedback session based on thefeature values. Optionally, the model was trained on samples generatedbased on (i) previous TH_(F) and m_(conf) of the user, and/or (ii)previous TH_(F) and m_(conf) of other users.

Various physiological responses may be detected based on thermalmeasurements and images of various regions of the face. In oneembodiment, a system configured to detect a physiological responseincludes an inward-facing head-mounted thermal camera (CAM), aninward-facing head-mounted visible-light camera (VCAM), and a computer.The system may optionally include additional elements such as a frameand additional cameras.

CAM is worn on a user's head and takes thermal measurements of a firstROI (TH_(ROI1)) on the user's face. Optionally, CAM weighs below 10 g.Optionally, CAM is located less than 15 cm from the user's face.Optionally, CAM utilizes a microbolometer or a thermopile sensor. In oneembodiment, CAM includes a focal-plane array (FPA) sensor and aninfrared lens, and the FPA plane is tilted by more than 2° relative tothe infrared lens plane according to the Scheimpflug principle in orderto improve the sharpness of the image of ROI₁ (where the lens planerefers to a plane that is perpendicular to the optical axis of the lens,which may include one or more lenses).

VCAM is worn on the user's head and takes images of a second ROI(IM_(ROI2)) on the user's face. Optionally, VCAM weighs below 10 g andis located less than 15 cm from the face. Optionally, ROI₁ and ROI₂overlap (which means extend over so as to cover at least partly). Forexample, ROI₂ may cover at least half of the area covered by ROI₁. Inone embodiment, VCAM includes a multi-pixel sensor and a lens, and thesensor plane is tilted by more than 2° relative to the lens planeaccording to the Scheimpflug principle in order to improve the sharpnessof the image of ROI₂.

It is to be noted that in some embodiments the system may be constructedin a way that none of the system's components (including the frame andcameras) occludes ROI₁ and/or ROI₂. In alternative embodiments, thesystem may be constructed in a way that at least some of the systemcomponents (e.g., the frame and/or CAM) may occlude ROI₁ and/or ROI₂.

The computer detects the physiological response based on TH_(ROI1),IM_(ROI2), and a model. Optionally, the model includes one or morethresholds to which TH_(ROI1) and/or IM_(ROI2) may be compared in orderto detect the physiological response. Optionally, the model includes oneor more reference time series to which TH_(ROI1) and/or IM_(ROI2) may becompared in order to detect the physiological response. Optionally, thecomputer detects the physiological response by generating feature valuesbased on TH_(ROI1) and IM_(ROI2), and utilizing the model to calculate,based on the feature values, a value indicative of the extent of thephysiological response. In this case, the model may be referred to as a“machine learning-based model”. Optionally, at least some of the featurevalues, which are generated based on IM_(ROI2) may be used to identify,and/or account for, various confounding factors that can alter TH_(ROI1)without being directly related to the physiological response. Thus, onaverage, detections of the physiological responses based on TH_(ROI1)and IM_(ROI2) are more accurate than detections of the physiologicalresponses based on TH_(ROI1) without IM_(ROI2).

In one example, the physiological response is indicative of anoccurrence of at least one of the following emotional states of theuser: joy, fear, sadness, and anger. In another example, thephysiological response is indicative of an occurrence of one or more ofthe following: stress, mental workload, an allergic reaction, aheadache, dehydration, intoxication, and a stroke. The physiologicalresponse may be a physiological signal of the user. In one example, thephysiological response is a heart rate of the user, and in this example,ROI₁ is on the skin above at least one of the superficial temporalartery and the frontal superficial temporal artery. In another example,the physiological response is frontal lobe brain activity of the user,and in this example, ROI₁ is on the forehead. In still another example,the physiological signal is a breathing rate of the user, and ROI₁ is onthe nasal area.

A machine learning-based model used to detect a physiological responseis typically trained on samples, where each sample includes featurevalues generated based on TH_(ROI1) and IM_(ROI2) taken during a certainperiod, and a label indicative of the physiological response of the userduring the certain period. Optionally, the model is trained on samplesgenerated based on measurements of the user (in which case the model maybe considered a personalized model of the user). Optionally, the modelis trained on samples generated based on measurements of one or moreother users. Optionally, the samples are generated based on measurementstaken while the user being measured was in different situations.Optionally, the samples are generated based on measurements taken ondifferent days.

In some embodiments, images such as IM_(ROI2) may be utilized togenerate various types of feature values, which may be utilized todetect the physiological response and/or detect an occurrence of aconfounding factor. Some of the feature values generated based on imagesmay include high-level facial-related feature values and theirderivatives, such as location and dimensions of facial features and/orlandmarks, identification of action units (AUs) in sequences of images,and/or blendshape weights. Other examples of features include variouslow-level features such as features generated using Gabor filters, localbinary patterns (LBP) and their derivatives, algorithms such as SIFTand/or SURF (and their derivatives), image keypoints, histograms oforiented gradients (HOG) descriptors, and statistical procedures suchindependent component analysis (ICA), principal component analysis(PCA), or linear discriminant analysis (LDA). Yet other examples offeature values may include features derived from multiple images takenat different times, such as volume local binary patterns (VLBP),cuboids, and/or optical strain-based features. Additionally, some of thefeature values may be based on other data, such as feature valuesgenerated based audio processing of data received from a head-mountedmicrophone. The audio processing may detect noises associated withtalking, eating, and drinking, and convert it to feature values to beprovided to the machine learning-based model.

Using both TH_(ROI1) and IM_(ROI2) to detect the physiological responsemay confer some advantages in some embodiments. For example, there maybe times when TH_(ROI1) and IM_(ROI2) provide complementing signals of aphysiological response (e.g., due to their ability to measuremanifestations of different physiological processes related to thephysiological response). This can increase the accuracy of thedetections. In one embodiment, in which the physiological response beingdetected is an emotional response, the computer may identify facialexpressions from IM_(ROI2), and detect the emotional response of theuser based on TH_(ROI1) and the identified facial expressions. Forexample, at least some of the feature values generated based onIM_(ROI2), which are used to detect the emotional response, areindicative of the facial expressions. Optionally, on average, detectionsof emotional responses based on both TH_(ROI1) and the identified facialexpressions are more accurate than detections of the emotional responsesbased on either TH_(ROI1) or the identified facial expressions.

The following are some specific examples how IM_(ROI2) may be utilizedto help make detections of a physiological response more accurate. Inone example, ROI₁ and ROI₂ are on the mouth, and IM_(ROI2) areindicative of a change in a facial expression during a certain periodthat involves a transition from a facial expression in which the lipsare in contact to a facial expression with an open mouth. Optionally, byutilizing IM_(ROI2) to detect the physiological response based onTH_(ROI1) taken during the certain period, the computer may be ableattribute a change in TH_(ROI1) to opening the mouth rather than achange in the temperature of the lips.

In another example, ROI₁ and ROI₂ are on the nose and upper lip, andIM_(ROI2) are indicative of a change in a facial expression during acertain period that involves a transition from a neutral facialexpression to a facial expression of disgust. Optionally, by utilizingIM_(ROI2) to detect the physiological response based on TH_(ROI1) takenduring the certain period, the computer may be able attribute a changein TH_(ROI1) to a raised upper lip and wrinkled nose instead of a changein the temperature of the nose and upper lip.

In yet another example, ROI₁ and ROI₂ are on the user's forehead locatedabout 1 cm above at least one of the user's eyebrows, and IM_(ROI2) areindicative of a change in a facial expression during a certain periodthat involves a transition from a neutral expression to a facialexpression involving raised and/or lowered eyebrows (includingmiddle-raised or middle-lowered eyebrows). Optionally, by utilizingIM_(ROI2) to detect the physiological response based on TH_(ROI1) takenduring the certain period, the computer may be able attribute a changein TH_(ROI1) to raising and/or lowering the eyebrows instead of a changein the temperature of the forehead.

It is to be noted that there are various approaches known in the art foridentifying facial expressions from images. While many of theseapproaches were originally designed for full-face frontal images, thoseskilled in the art will recognize that algorithms designed for full-facefrontal images may be easily adapted to be used with images obtainedusing the inward-facing head-mounted visible-light cameras disclosedherein. For example, the various machine learning techniques describedin prior art references may be applied to feature values extracted fromimages that include portions of the face from orientations that are notdirectly in front of the user. Furthermore, due to the closeness of VCAMto the face, facial features are typically larger in images obtained bythe systems described herein. Moreover, challenges such as imageregistration and face tracking are vastly simplified and possiblynon-existent when using inward-facing head-mounted cameras. Thereference Zeng, Zhihong, et al. “A survey of affect recognition methods:Audio, visual, and spontaneous expressions.” IEEE transactions onpattern analysis and machine intelligence 31.1 (2009): 39-58, describessome of the algorithmic approaches that may be used for this task.

In some embodiments, TH_(ROI1) and IM_(ROI2) may provide different andeven possibly contradicting indications regarding the physiologicalresponse. In particular, facial expressions may not always express how auser truly feels. For example, when in company of other people, a usermay conceal his or her true feelings by making non-genuine facialexpressions. However, at the same time, thermal measurements of theuser's face may reveal the user's true emotions. Thus, a system thatrelies only on IM_(ROI2) to determine the user's emotional response maybe mistaken at times, and using TH_(ROI1) can help make detections moreaccurate.

In one example, responsive to receiving a first set of TH_(ROI1) andIM_(ROI2) taken during a first period in which the user expressed acertain facial expression, the computer detects a first emotionalresponse of the user. Additionally, responsive to receiving a second setof TH_(ROI1) and IM_(ROI2) taken during a second period in which theuser expressed again the certain facial expression, the computer detectsa second emotional response of the user, which is not the same as thefirst emotional response. The computer detected different emotionalresponses in this example because TH_(ROI1) of the first set areindicative of a first physiological response, while TH_(ROI1) of thesecond set are indicative of a second physiological response. Followingare some more detailed examples of situations in which this may occur.

In one example, the first set includes IM_(ROI2) indicative of a facialexpression that is a smile and TH_(ROI1) indicative of stress below acertain threshold, and the first emotional response detected by thecomputer is happiness. The second set in this example includes IM_(ROI2)indicative of a facial expression that is a smile and TH_(ROI1)indicative of stress above the certain threshold, and the secondemotional response detected by the computer is discomfort.

In another example, the first set includes IM_(ROI2) indicative of afacial expression that is a neutral expression and TH_(ROI1) indicativeof stress below a certain threshold, and the first emotional responsedetected by the computer is comfort. The second set includes IM_(ROI2)indicative of a facial expression that is neutral and TH_(ROI1)indicative of stress above the certain threshold, and the secondemotional response detected by the computer is concealment.

In yet another example, the first set includes IM_(ROI2) indicative of afacial expression that is an expression of anger and TH_(ROI1)indicative of stress above a certain threshold, and the first emotionalresponse detected by the computer is anger. The second set includesIM_(ROI2) indicative of a facial expression that is an expression ofanger and TH_(ROI1) indicative of stress below the certain threshold,and the second emotional response detected by the computer is indicativeof pretending to be angry.

The phenomenon of making different detections based on thermalmeasurements compared to the emotional response that is visible in afacial expression is illustrated in FIG. 44a and FIG. 44b . Theillustrated figures include an HMS with CAM 514 and VCAM 515 that maycover portions of a cheek, mouth and/or nose. FIG. 44a illustrates acase in which the user's smiling face may be mistaken for happiness;however, the cold nose indicates that the user is in fact stressed. FIG.44b illustrates a case in which the facial expression indicates that theuser is in a neutral state; however, the warm nose indicates that theuser is excited. FIG. 44a and FIG. 44b also illustrate a second CAM 516and a second VCAM 517, which may be utilized in some embodiments, asdescribed herein.

FIG. 45 illustrates one embodiment of a smartphone app that provides theuser a feedback about how he/she looks to others. The illustrated appshows that the user was happy 96 time and angry 20 times. Because thepurpose of this app is to measure how the user looks to others, thecomputer counts the facial expressions based on IM_(ROI2) withoutcorrecting the facial expressions according TH_(ROI1).

FIG. 46 illustrates one embodiment of a tablet app that provides theuser a feedback about how he/she felt during a certain period (e.g.,during the day, the week, or while being at a certain location). Theillustrated app shows that the user felt sad 56 minutes and happy 135minutes. Because the purpose of this app is to measure how the userfeels (and not just how the user looks to others), the computerdetermines the user's emotional state based on a combined analysis ofIM_(ROI2) and TH_(ROI1), as exemplified above.

In one embodiment, the system may include a second inward-facinghead-mounted thermal camera (CAM2) that takes thermal measurements of athird ROI (TH_(ROI1)) on the face. Optionally, CAM2 weighs below 10 gand is physically coupled to the frame. Optionally, the center of ROI₁is to the right of the center of the third region of interest (ROI₃),and the symmetric overlapping between ROI₁ and ROI₃ is above 50%.Optionally, to detect the physiological response, the computer accountsfor facial thermal asymmetry, based on a difference between TH_(ROI1)and TH_(ROI1).

It is noted that the symmetric overlapping is considered with respect tothe vertical symmetry axis that divides the face to the right and leftportions. The symmetric overlapping between ROI₁ and ROI₃ may beobserved by comparing the overlap between ROI₁ and a mirror image ofROI₃, where the mirror image is with respect to a mirror that isperpendicular to the front of the face and whose intersection with theface is along the vertical symmetry axis (which goes through the middleof the forehead and the middle of the nose).

Some examples of calculations that may be performed by the computer toaccount for thermal asymmetry include: (i) utilizing differentthresholds to which TH_(ROI1) and TH_(ROI3) are compared; (ii) utilizingdifferent reference time series to which TH_(ROI1) and TH_(ROI1) arecompared; (iii) utilizing a machine learning-based model that providesdifferent results for first and second events that involve the sameaverage change in TH_(ROI1) and TH_(ROI3) with different extents ofasymmetry in TH_(ROI1) and TH_(ROI1); and (iv) utilizing the asymmetryfor differentiating between (a) temperature changes in TH_(ROI1) andTH_(ROI1) that are related to the physiological response and (b)temperature changes in TH_(ROI1) and TH_(ROI3) that are unrelated to thephysiological response.

In one embodiment, the system may include a second inward-facinghead-mounted visible-light camera (VCAM2) that takes images of a thirdROI (IM_(ROI3)) on the face. Optionally, VCAM2 weighs below 10 g and isphysically coupled to the frame. Optionally, VCAM and VCAM2 are locatedat least 0.5 cm to the right and to the left of the vertical symmetryaxis that divides the face, respectively, and the symmetric overlappingbetween ROI₂ and ROI₃ is above 50%. Optionally, the computer detects thephysiological response also based on IM_(ROI3). For example, thecomputer may generate some feature values based on IM_(ROI3), which maybe similar to feature values generated based on IM_(ROI2), and utilizesthe some feature values in the detection of the physiological response.In another example, the computer detects the physiological responsebased on the extent of symmetry between symmetric facial elementsextracted from IM_(ROI2) and IM_(ROI1).

In some embodiments, IM_(ROI2) may include recognizable facial skincolor changes (FSCC). FSCC are typically a result of changes in theconcentration levels of hemoglobin and blood oxygenation under a user'sfacial skin, and are discussed in more detail elsewhere in thisdisclosure. In one embodiment, the computer calculates, based onIM_(ROI2), a value indicative of FSCC, and detects an emotional state ofthe user based on the calculated value. Optionally, on average,detections of the physiological response based on both TH_(ROI1) andFSCC are more accurate than detections of the physiological responsebased on either TH_(ROI1) or FSCC. In another embodiment, the computergenerates feature values that are indicative of FSCC in IM_(ROI2), andutilizes a model to detect the physiological response based on thefeature values. Optionally, at least some of the feature values aregenerated based on TH_(ROI1). Optionally, the model was trained based onsamples, with each sample including feature values generated based oncorresponding measurements of the user and a label indicative of thephysiological response. Optionally, the label may be derived, forexample, from analysis of the user's speech/writing, facial expressionanalysis, speech emotion analysis, and/or emotion extraction fromanalyzing galvanic skin response (GSR) and heart rate variability (HRV).

IM_(ROI2) may be utilized, in some embodiments, to detect occurrences ofconfounding factors that can affect the temperature on the face, but areunrelated to the physiological response being detected. Thus,occurrences of confounding factors can reduce the accuracy of detectionsof the physiological response based on thermal measurements (such asbased on TH_(ROI1)). Detecting occurrences of the confounding factorsdescribed below (cosmetics, sweat, hair, inflammation and touching) maybe done utilizing various image-processing and/or image-analysistechniques known in the art. For example, detecting occurrences of atleast some of the confounding factors described below may involve amachine learning algorithm trained to detect the confounding factors,and/or comparing IM_(ROI2) to reference images that involve and do notinvolve the confounding factor (e.g., a first set of reference IM_(ROI2)in which makeup was applied to the face and a second set of referenceIM_(ROI2) in which the face was bare of makeup).

The computer may utilize detection of confounding factors in variousways in order to improve the detection of the physiological responsebased on TH_(ROI1). In one embodiment, the computer may refrain frommaking a detection of the physiological response responsive toidentifying that the extent of a certain confounding factor reaches athreshold. For example, certain physiological responses may not bedetected if there is extensive facial hair on the face or extensive skininflammation In another embodiment, the model used to detect thephysiological response may include a certain feature that corresponds toa certain confounding factor, and the computer may generate a certainfeature value indicative of the extent of the certain confoundingfactor. Optionally, the model in this case may be trained on samples inwhich the certain feature has different values, such as some of thesamples used to train the model are generated based on measurementstaken while the certain confounding factor occurred, and other samplesused to train the model were generated based on measurements taken whilethe certain confounding factor did not occur. In yet another embodiment,the computer may weight measurements based on the occurrence ofconfounding factors, such that measurements taken while certainconfounding factors occurred, may be given lower weights thanmeasurements taken while the certain confounding factor did not occur.Optionally, lower weights for measurements mean that they have a smallerinfluence on the detection of the physiological response thanmeasurements with higher weights. The following are some examples ofconfounding factors that may be detected, in some embodiments, based onIM_(ROI2).

Some types of cosmetics (e.g., makeup and/or cream) may mask an ROI,affect the ROI's emissivity, and/or affect the ROI's temperature. Thus,taking into account cosmetics as a confounding factor may improve thesystem's ability to detect the physiological response. In oneembodiment, the model was trained on: samples generated based on a firstset of TH_(ROI1) and IM_(ROI2) taken after cosmetics were applied to aportion of the overlapping region between ROI₁ and ROI₂, and othersamples generated based on a second set of TH_(ROI1) and IM_(ROI2) takenwhile the overlapping region was bare of cosmetics. Optionally,utilizing this model may enable the computer to account for presence ofcosmetics on a portion of ROI₂.

Sweating may affect the ROI's emissivity. Thus, taking into accountsweating as a confounding factor may improve the system's ability todetect the physiological response. In one embodiment, the model wastrained on: samples generated from a first set of TH_(ROI1) andIM_(ROI2) taken while sweat was detectable on a portion of theoverlapping region between ROI1 and ROI2, and additional samplesgenerated from a second set of TH_(ROI1) and IM_(ROI2) taken while sweatwas not detectable on the overlapping region. Optionally, utilizing thismodel may enable the computer to account for sweat on the overlappingregion.

Dense hair may affect the ROI's emissivity, which may make the ROIappear, in thermal imaging, colder than it really is. Thus, taking intoaccount hair density and/or hair length (both referred to as hairdensity) as a confounding factor may improve the system's ability todetect the physiological response. In one embodiment, the model wastrained on: samples generated from a first set of TH_(ROI1) andIM_(ROI2) taken while hair density on a portion of the overlappingregion between ROI₁ and ROI₂ was at a first level, and additionalsamples generated from a second set of TH_(ROI1) and IM_(ROI2) takenwhile hair density on the portion of the overlapping region between ROI₁and ROI₂ was at a second level higher than the first level. Optionally,utilizing a model trained so may enable the computer to account for hairon the overlapping region.

In another embodiment, when the hair can be moved the system may requestthe user to move her hair in order to enable the thermal cameras to takecorrect measurements. For example, FIG. 50a illustrates a first casewhere the user's hair does not occlude the forehead. FIG. 50billustrates a second case where the user's hair does occlude theforehead and thus the system requests the user to move the hair in orderto enable correct measurements of the forehead.

Skin inflammations (which may include skin blemishes, acne, and/orinflammatory skin diseases) usually increases ROI temperature in amanner that is unrelated to the physiological response being detected.Thus, taking into account skin inflammation as a confounding factor mayimprove the system's ability to detect the physiological response. FIG.48a illustrates heating of the ROI because of sinusitis, for which thesystem detects the physiological response (sinusitis). On the otherhand, FIG. 48b illustrates heating of the same ROI because of acne, forwhich the system does detect sinusitis. In one embodiment, the model wastrained on: samples generated from a first set of TH_(ROI1) andIM_(ROI2) taken while skin inflammation was detectable on a portion ofthe overlapping region between ROI₁ and ROI₂, and additional samplesgenerated from a second set of TH_(ROI1) and IM_(ROI2) taken while skininflammation was not detectable on the overlapping region. Optionally,utilizing a model trained so may enable the computer to account for skininflammation on the overlapping region.

Touching the ROI may affect TH_(ROI) by increasing or decreasing thetemperature at the touched region. Thus, touching the ROI may beconsidered a confounding factor that can make detections of thephysiological response less accurate. In one embodiment, the model wastrained on: samples generated from a first set of TH_(ROI1) andIM_(ROI2) taken while detecting that the user touches a portion of theoverlapping region between ROI₁ and ROI₂, and additional samplesgenerated from a second set of TH_(ROI1) and IM_(ROI2) taken whiledetecting that the user does not touch the overlapping region.Optionally, utilizing a model trained so may enables the computer toaccount for touching the overlapping region.

Throughout day-to-day activities, a user may make various facialmovements that are unrelated to the physiological response beingdetected, and thus can negatively affect the thermal measurements takenby CAM. This can lead to measurements that may be incorrectly attributedto the physiological response. To address this issue, the computer mayidentify disruptive activities, such as talking, eating, and drinking,and utilize the identified disruptive activities in order to moreaccurately detect the physiological response. In one embodiment, thecomputer utilizes a machine learning-based approach to handle thedisruptive activities. This approach may include (i) identifying, basedon IM_(ROI2), occurrences of one or more of the disruptive activities,(ii) generating feature values based on the identified disruptiveactivities, and (iii) utilizing a machine learning-based model to detectthe physiological response based on the feature values and featurevalues generated from TH_(ROI1).

In addition to detecting a physiological response, in some embodiments,the computer may utilize IM_(ROI2) to generate an avatar of the user(e.g., in order to represent the user in a virtual environment).Optionally, the avatar may express emotional responses of the user,which are detected based on IM_(ROI2). Optionally, the computer maymodify the avatar to show synthesized facial expressions that are notmanifested in the user's actual facial expressions, but the synthesizedfacial expressions correspond to emotional responses detected based onTH_(ROI1). Some of the various approaches that may be utilized togenerate the avatar based on IM_(ROI2) are described in co-pending USpatent publication 2016/0360970.

Contraction and relaxation of various facial muscles can cause facialtissue to slightly change its position and/or shape. Thus, facialmovements can involve certain movements to ROIs. With thermal camerasthat have multiple sensing elements (pixels), this can cause the ROI tomove and be covered by various subsets of pixels as the user's facemoves (e.g., due to talking/or making facial expressions). For example,smiling can cause the user's cheeks to move upwards. This can cause athermal camera that covers a cheek to capture an ROI located on a cheekwith a first set of pixels (from among the camera's pixels) when theuser has a neutral expression, and to capture images of the ROI with asecond set of pixels, when the user is smiling. In this example, onaverage, the pixels in the second set are likely to be located higher inthe images than the pixels in the first set. To account for the possiblemovement of ROIs due to facial expressions, the computer may tracklocations of one or more facial landmarks in a series of IM_(ROI2), andutilize the locations to adjust TH_(ROI1). Facial landmarks are usuallythe most salient facial points on the face.

In one embodiment in which CAM comprises multiple sensing elements,which correspond to values of multiple pixels in TH_(ROI1), the computermay assign weights to the multiple pixels based on the locations of theone or more facial landmarks, which are determined based on IM_(ROI2).Assigning weights to pixels based on their location with respect to afacial landmark can be considered a form of selection of the pixels thatcover the ROI based on the location of the landmark. In one example, theweights are assigned based on a function that takes into account thedistance of each pixel from the locations of one or more faciallandmarks and/or the relative position of each pixel with respect to thelocations.

In another embodiment, the computer may generate certain feature valuesbased on locations of one or more landmarks, which are determined basedon analysis of IM_(ROI2). These certain feature values may be utilizedin conjunction with other feature values (e.g., feature values generatedbased on TH_(ROI1)) to detect the physiological response using a machinelearning-based model.

The following is a description of a method for detecting a physiologicalresponse based on measurements from CAM and VCAM. The steps describedbelow may be performed by running a computer program having instructionsfor implementing the method. Optionally, the instructions may be storedon a computer-readable medium, which may optionally be a non-transitorycomputer-readable medium. In response to execution by a system includinga processor and memory, the instructions cause the system to perform thefollowing steps: In Step 1, taking thermal measurements of a first ROI(TH_(ROI1)) on the user's face using an inward-facing head-mountedthermal camera located at most 15 cm from the user's face. In Step 2,taking images of a second ROI (IM_(ROI2)) on the user's face with aninward-facing head-mounted visible-light camera located at most 15 cmfrom the user's face. Optionally, the first ROI (ROI₁) and the secondROI (ROI₂) overlap. In Step 3, generating feature values based onTH_(ROI1) and IM_(ROI2). And in Step 4, utilizing a model to detect thephysiological response based on the feature values. Optionally, themodel was trained based on previous TH_(ROI1) and IM_(ROI2) taken ondifferent days.

In one embodiment, the physiological response is an emotional response,and the method optionally includes the following steps: calculating,based on IM_(ROI2), a value indicative of facial skin color changes(FSCC), and utilizing the value indicative of FSCC to generate at leastone of the feature values used to detect the physiological response inStep 4.

In another embodiment, generating the feature values in Step 3 involvesgenerating, based on IM_(ROI2), feature values indicative of anoccurrence of one or more of the following confounding factors on aportion of the overlapping region between ROI₁ and ROI₂: a presence ofcosmetics, a presence of sweat, a presence of hair, and a presence ofskin inflammation.

The following is a description of a system that detects a physiologicalresponse based on an inward-facing head-mounted thermal camera(CAM_(in)), an outward-facing head-mounted thermal camera (CAM_(out)),and a computer. CAM_(out) measures the environment and generates dataindicative of confounding factors, such as direct sunlight or airconditioning. Accounting for confounding factors enables the system tomore accurately detect the physiological response compared to a systemthat does not account for these confounding factors. Optionally,CAM_(in) and/or CAM_(out) are physically coupled to a frame worn on auser's head, such as a frame of a pair of eyeglasses or an augmentedreality device. Optionally, each of CAM_(in) and CAM_(out) weighs below5 g and is located less than 15 cm from the user's face.

CAM_(in) takes thermal measurements of an ROI (TH_(ROI)) on the user'sface. Optionally, CAM_(in) does not occlude the ROI. In one example, theROI includes a region on the forehead and the physiological responseinvolves stress, a headache, and/or a stroke. In another example, theROI includes a region on the nose and the physiological response is anallergic reaction.

CAM_(out) takes thermal measurements of the environment (TH_(ENV)).Optionally, CAM_(out) does not occlude the ROI. Optionally, the anglebetween the optical axes of CAM_(in) and CAM_(out) is at least 45°, 90°,130°, 170°, or 180°. Optionally, the field of view (FOV) of CAM_(in) islarger than the FOV of CAM_(out) and/or the noise equivalentdifferential temperature (NEDT) of CAM_(in) is lower than NEDT ofCAM_(out). In one example, CAM_(in) has a FOV smaller than 80° andCAM_(out) has a FOV larger than 80°. In another example, CAM_(in) hasmore sensing elements than CAM_(out) (e.g., CAM_(in) has at least doublethe number of pixels as CAM_(out)).

In one embodiment, CAM_(in) and CAM_(out) are based on sensors of thesame type with similar operating parameters. Optionally, CAM_(in) andCAM_(out) are located less than 5 cm or 1 cm apart. Having sensors ofthe same type, which are located near each other, may have an advantageof having both CAM_(in) and CAM_(out) be subject to similar inaccuraciesresulting from heat conductance and package temperature. In anotherembodiment, CAM_(in) and CAM_(out) may be based on sensors of differenttypes, with different operating parameters. For example, CAM_(in) may bebased on a microbolometer FPA while CAM_(out) may be based on athermopile (that may be significantly less expensive than themicrobolometer).

FIG. 51a illustrates one embodiment of the system that includesinward-facing and outward-facing head-mounted thermal cameras on bothsides of the frame. In this illustration, CAM_(in) is the inward-facingthermal camera 12, which takes thermal measurements of ROI 13, andCAM_(out) is the outward-facing thermal camera 62. Arc 64 illustratesthe larger FOV of CAM_(out) 62, compared to the FOV of CAM_(in) thatcovers ROI 13. The illustrated embodiment includes a second head-mountedthermal camera 10 (CAM_(in2)) on the right side of the frame, whichtakes thermal measurements of ROI 11, and a second outward-facinghead-mounted thermal camera 63 (CAM_(out2)). FIG. 51b illustratesreceiving an indication on a GUI (on the illustrated laptop) that theuser is not monitored in direct sunlight. Cameras 520 and 521 are theoutward-facing head-mounted thermal cameras.

The computer detects a physiological response based on TH_(ROI) andTH_(ENV). Optionally, TH_(ENV) are utilized to account for at least someof the effect of heat transferred from the environment to the ROI (andnot due to the user's physiological response). Thus, on average,detections of the physiological response based on TH_(ROI) and TH_(ENV)may be more accurate than detections of the physiological response basedon TH_(ROI) without TH_(ENV).

There are various ways in which the computer may utilize TH_(ENV) toincrease the accuracy of detecting the physiological response. In oneembodiment, the computer generates feature values based on a set ofTH_(ROI) and TH_(ENV), and utilizes a machine learning-based model todetect, based on the feature values, the physiological response. Byutilizing TH_(ENV) to generate one or more of the feature values, thecomputer may make different detections of the physiological responsebased on similar TH_(ROI) that are taken in dissimilar environments. Forexample, responsive to receiving a first set of measurements in whichTH_(ROI) reaches a first threshold while TH_(ENV) does not reach asecond threshold, the computer detects the physiological response.However, responsive to receiving a second set of measurements in whichTH_(ROI) reaches the first threshold while TH_(ENV) reaches the secondthreshold, the computer does not detect the physiological response.Optionally, TH_(ENV) reaching the second threshold indicates that theuser was exposed to high infrared radiation that is expected tointerfere with the detection.

In another embodiment, the computer may utilize TH_(ENV) for theselection of values that are appropriate for the detection of thephysiological response. In one example, the computer may selectdifferent thresholds (to which TH_(ROI) are compared) for detecting thephysiological response. In this example, different TH_(ENV) may causethe computer to use different thresholds. In another example, thecomputer may utilize TH_(ENV) to select an appropriate reference timeseries (to which TH_(ROI) may be compared) for detecting thephysiological response. In yet another example, the computer may utilizeTH_(ENV) to select an appropriate model to utilize to detect thephysiological response based on the feature values generated based onTH_(ROI).

In still another embodiment, the computer may normalize TH_(ROI) basedon TH_(ENV). In one example, the normalization may involve subtracting avalue proportional to TH_(ENV) from TH_(ROI), such that the value of thetemperature at the ROI is adjusted based on the temperature of theenvironment at that time and/or in temporal proximity to that time(e.g., using an average of the environment temperature during thepreceding minute). Additionally or alternatively, the computer mayadjust weights associated with at least some TH_(ROI) based on TH_(ENV),such that the weight of measurements from among TH_(ROI) that were takenduring times the measurements of the environment indicated extremeenvironmental temperatures is reduced.

In yet another embodiment, responsive to determining that TH_(ENV)represent an extreme temperature (e.g., lower than 5° C., higher than35° C., or some other ranges deemed inappropriate temperatures), thecomputer may refrain from performing detection of the physiologicalresponse. This way, the computer can avoid making a prediction that isat high risk of being wrong due to the influence of the extremeenvironmental temperatures. In a similar manner, instead of determiningthat TH_(ENV) represent an extreme temperature, the computer maydetermine that the difference between TH_(ROI) and TH_(ENV) are not inan acceptable range (e.g., there is a difference of more than 15° C.between the two), and refrain from making a detection of thephysiological response in that event.

The following examples describe ways to use TH_(ENV) to detect thephysiological response based on TH_(ROI). In one example, the computerdetects the physiological response based on a difference betweenTH_(ROI) and TH_(ENV), which enables the system to operate well in anuncontrolled environment that does not maintain environmentaltemperature in a range below ±1° C. and does not maintain humidity in arange below ±3%. In another example, the computer detects thephysiological response by performing the following steps: calculating atemperature difference between TH_(ROI) and TH_(ENV) taken at time x(ΔT_(x)), calculating a temperature difference between TH_(ROI) andTH_(ENV) taken at time y (ΔT_(y)), and detecting the physiologicalresponse based on a difference between ΔT_(x) and ΔT_(y). Optionally,detecting the physiological response is based on the difference betweenΔT_(x) and ΔT_(y) reaching a predetermined threshold. Optionally, thepredetermined threshold is selected from a threshold in the time domain,and/or a threshold in the frequency domain Optionally, the magnitude ofthe difference between ΔT_(x) and ΔT_(y) is indicative of an extent ofthe physiological response. It is noted that sentences such as“calculating a difference between M and N” or “detecting a differencebetween M and N” are intended to cover any function that is proportionalto the difference between M and N.

Because the FOV of CAM_(out) is limited and the responsivity ofCAM_(out) decreases when drawing away from the optical axis, it may bebeneficial to utilize two or more CAM_(out) pointed at different angles.

In one embodiment, the system may include a second outward-facinghead-mounted thermal camera (CAM_(out2)), which takes thermalmeasurements of the environment (TH_(ENV2)). Optionally, there is anangle of at least 30° between the optical axes of CAM_(out) andCAM_(out2) Utilizing two or more outward-facing head-mounted thermalcameras such as CAM_(out) and CAM_(out2) can help identify cases inwhich there is a directional environmental interference (e.g., sunlightcoming from a certain direction). In some cases, such a directionalinterference can lead to refraining from making a detection of thephysiological response. For example, responsive to receiving a first setof measurements in which TH_(ROI) reach a first threshold while thedifference between TH_(ENV) and TH_(ENV2) does not reach a secondthreshold, the computer detects the physiological response. However,responsive to receiving a second set of measurements in which TH_(ROI)reach the first threshold while the difference between TH_(ENV) andTH_(ENV2) reaches the second threshold, the computer does not detect thephysiological response. Optionally, the computer detects thephysiological response based on a difference between TH_(ROI), TH_(ENV),and TH_(ENV2), while taking into account the angle between the opticalaxes of CAM_(out) and CAM_(out2) and a graph of responsivity as functionof the angle from the optical axes of each of CAM_(out) and CAM_(out2).

In another embodiment, CAM_(in) and CAM_(out) are located to the rightof the vertical symmetry axis that divides the user's face, and the ROIis on the right side of the face. Optionally, the system includes asecond inward-facing head-mounted thermal camera (CAM_(in2)) and asecond outward-facing head-mounted thermal camera (CAM_(out2)) locatedto the left of the vertical symmetry axis. CAM_(in2) takes thermalmeasurements of a second ROI (TH_(ROI2)) on the left side of the face,and does not occlude the second ROI (ROI₂). CAM_(out2) takes thermalmeasurements of the environment (TH_(ENV2)) that is more to the leftrelative to TH_(ENV). In this embodiment, the computer detects thephysiological response also based on TH_(ROI2) and TH_(ENV2).

In still another embodiment, the optical axes of CAM_(in) and CAM_(out)are above the Frankfort horizontal plane, and the system furtherincludes a second inward-facing head-mounted thermal camera (CAM_(in2))and a second outward-facing head-mounted thermal camera (CAM_(out2)),located such that their optical axes are below the Frankfort horizontalplane, which take thermal measurements TH_(ROI2) and TH_(ENV2),respectively. In this embodiment, the computer detects the physiologicalresponse also based on TH_(ROI2) and TH_(ENV2).

Optionally, the computer detects the physiological response byperforming at least one of the following calculations: (i) when thedifference between TH_(ENV) and TH_(ENV2) reaches a threshold, thecomputer normalizes TH_(ROI) and TH_(ROI2) differently against thermalinterference from the environment, (ii) when TH_(ENV) does not reach apredetermined threshold for thermal environmental interference, whileTH_(ENV2) reaches the predetermined threshold, the computer assignsTH_(ROI) a higher weight than TH_(ROI2) for detecting the physiologicalresponse, and (iii) the computer generates feature values based onTH_(ROI), TH_(ENV), TH_(ENV2) and optionally TH_(ROI2) and utilizes amodel to detect, based on the feature values, the physiologicalresponse. Optionally, the model was trained based on a first set ofTH_(ROI), TH_(ROI2), TH_(ENV) and TH_(ENV2) of one or more users takenwhile the one or more users had the physiological response, and a secondset of TH_(ROI), TH_(ROI2), TH_(ENV) and TH_(ENV2) of the one or moreusers taken while the one or more users did not have the physiologicalresponse.

In addition to having one or more CAM_(out), or instead of having theone or more CAM_(out), some embodiments may include a sensor that may beused to address various other confounding factors, such as usermovements and wind, which are discussed below. Optionally, the sensor iscoupled to a frame worn on the user's head. An example of such a sensoris sensor 68 in FIG. 51 a.

In one embodiment, the sensor takes measurements (denoted m_(conf)) thatare indicative of an extent of the user's activity, an orientation ofthe user's head, and/or a change in a position of the user's body. Forexample, the sensor may be (i) a movement sensor that is physicallycoupled to a frame worn on the user's head, or coupled to a wearabledevice worn by the user, (ii) a visible-light camera that takes imagesof the user, and/or (iii) an active 3D tracking device that emitselectromagnetic waves and generates 3D images based on receivedreflections of the emitted electromagnetic waves. Optionally, thecomputer detects the physiological response also based on m_(conf). Inone example, the computer may refrain from detecting the physiologicalresponse if m_(conf) reaches a threshold (which may indicate the userwas very active which causes an increase in body temperature). Inanother example, the computer generates feature values based onTH_(ROI), TH_(ENV), and m_(conf) and utilizes a model to detect thephysiological response based on the feature values. Optionally, themodel was trained based on previous TH_(ROI), TH_(ENV), and m_(conf)taken while the user had different activity levels. For example, themodel may be trained based on: a first set of previous TH_(ROI),TH_(ENV), and m_(conf) taken while the user was walking or running, anda second set of previous TH_(ROI), TH_(ENV), and m_(conf) taken whilethe user was sitting or standing.

FIG. 53 illustrates an elderly person whose facial temperature increasesas a result of bending the head down towards the floor. In this example,the system receives an indication of the user's action via the sensor(e.g., one or more gyroscopes) and consequently refrains fromerroneously detecting certain physiological responses, since theincrease in temperature may be attributed to the person being bent over.In one embodiment, a sensor provides indications indicative of bendingthe head down above a certain degree from the normal to earth, wherebending the head down above the certain degree is expected to cause achange in TH_(ROI). The computer generates feature values based onTH_(ROI), TH_(ENV), and m_(conf), and utilizes a model to detect thephysiological response based on the feature values. The model wastrained based on: a first set of previous TH_(ROI), TH_(ENV), andm_(conf) taken while a user was bending the head down above the certaindegree, and a second set of previous TH_(ROI), TH_(ENV), and m_(conf)taken while the user was not bending the head down above the certaindegree.

In another embodiment, the sensor may be an anemometer that isphysically coupled to a frame worn on the user's head, is located lessthan 15 cm from the face, and provides a value indicative of a speed ofair directed at the face (m_(wind)). Optionally, the computer detectsthe physiological response also based on m_(wind). In one example, thecomputer refrains from detecting the physiological response if m_(wind)reaches a threshold (which may indicate that the user was in anenvironment with strong wind that may excessively cool regions on theface). In another example, the computer generates feature values basedon TH_(ROI), TH_(ENV), and m_(wind) and utilizes a model to detect,based on the feature values, the physiological response. FIG. 52illustrates a case in which a user receives an indication that she isnot being monitored in a windy environment. Optionally, the model wastrained based on previous TH_(ROI), TH_(ENV), and m_(wind) taken while auser was in different environments. For example, the model may betrained based on: a first set of previous TH_(ROI3) TH_(ENV), andm_(wind) taken while being indoors, and a second set of previousTH_(ROI), TH_(ENV), and m_(wind) taken while being outdoors.

The following is a method for detecting a physiological response whiletaking into account a confounding factor that involves environmentalthermal interferences (e.g., direct sunlight). Having differentenvironmental conditions may cause a system such as the one illustratedin FIG. 51a to behave differently, as shown in the steps below. Thesteps described below may be performed by running a computer programhaving instructions for implementing the method. Optionally, theinstructions may be stored on a computer-readable medium, which mayoptionally be a non-transitory computer-readable medium. In response toexecution by a system including a processor and memory, the instructionscause the system to perform the following steps: In Step 1, takingthermal measurements of a region of interest (TH_(ROI)) on a user's faceutilizing an inward-facing head-mounted thermal camera (CAM_(in)) wornby the user. In step 2, taking thermal measurements of the environment(TH_(ENV)) utilizing an outward-facing head-mounted thermal camera(CAM_(out)) worn by the user. In step 3, generating feature values basedon TH_(ROI) and TH_(ENV). And in step 4, utilizing a machinelearning-based model to detect the physiological response based on thefeature values.

The method may optionally further include the following steps: taking afirst set of TH_(ROI) (first TH_(ROI)), where the first set of TH_(ROI)reach a first threshold; taking a first set of TH_(ENV) (firstTH_(ENV)), where the first set of TH_(ENV) do not reach a secondthreshold; detecting, based on the first set of TH_(ROI) and the firstset of TH_(ENV), that the user had the physiological response; taking asecond set of TH_(ROI), where the second set of TH_(ROI) reach the firstthreshold; taking a second set of TH_(ENV), where the second set ofTH_(ENV) reach the second threshold; and detecting, based on the secondset of TH_(ROI) and the second set of TH_(ENV), that the user did nothave the physiological response. Optionally, the method furtherincludes: taking a third set of TH_(ROI), where the third set ofTH_(ROI) do not reach the first threshold; taking a third set ofTH_(ENV), where the third set of TH_(ENV) do not reach the secondthreshold; and detecting, based on the third set of TH_(ROI) and thethird set of TH_(ENV), that the user did not have the physiologicalresponse.

The following is a description of a system for detecting a physiologicalresponse, which includes a CAM and a sensor. The sensor providesmeasurements indicative of times at which the user touches the face.Touching the face can warm certain regions of the face, and the systemmay utilize these measurements in order to account for such cases. Thus,the system may more accurately detect the physiological responsecompared to systems that do not account for touching of the face.

CAM is worn on the user's head and takes thermal measurements of an ROI(TH_(ROI)) on the user's face. Optionally, the system includes a frameto which CAM and the sensor may be physically coupled. Optionally, CAMis located less than 15 cm from the face and/or weighs below 10 g.

The sensor provides measurements (M) indicative of times at which theuser touches the ROI. The user may touch the ROI using/with a finger,the palm, a tissue or a towel held by the user, a makeup-related itemheld by the user, and/or a food item eaten by the user. Touching the ROImay affect TH_(ROI) by increasing or decreasing the temperature at thetouched region. Thus, touching the ROI may be considered a confoundingfactor that can make detections of the physiological response by acomputer less accurate. M may include values measured by the sensorand/or results of processing of values measured by the sensor. Varioustypes of sensors may be utilized in different embodiments to generate M,such as: a visible-light camera (where the computer uses imageprocessing to identify touching the ROI), a miniature radar (such aslow-power radar operating in the range between 30 GHz and 3,000 GHz,where the computer uses signal processing of the reflections to identifytouching the ROI), a miniature active electro-optics distancemeasurement device, and/or an ultrasound sensor.

In some embodiments, the sensor may be unattached to a frame worn on theuser's head. For example, the sensor may include a visible-light cameramounted to an object in the user's environment (e.g., a laptop), and maynormally located at a distance greater than 20 cm from the user's face.Optionally, the computer may utilize M to determine when it is likely(but not necessarily certain) that the user touched the face. In oneexample, the sensor includes a movement-measuring device embedded in abracelet, and the computer increases the probability for a physicalcontact with the face when the user's hand is estimated to be at facelevel and/or close to the face. In another example, the sensor includesan altimeter embedded in a bracelet, and the computer increases theprobability for an event of physical contact with the face when theuser's hand is estimated to be at face level.

FIG. 49a and FIG. 49b illustrate one embodiment of a system thatprovides indications when the user touches his/her face. The systemincludes a frame 533, head-mounted sensors (530, 531, 532) able todetect touching the face, and head-mounted thermal cameras (534, 535,536, 537). Optionally, the head-mounted sensors are visible-lightcameras that take images of the ROIs. Head-mounted sensor 530 capturesan ROI above the frame, and head-mounted sensors 531 and 532 captureROIs below the frame. Hot spot 538, which is measured by the thermalcamera 534, was caused by touching the forehead and is unrelated to thephysiological response being detected. Upon detecting touching of theROI, the computer may use the associated thermal measurementsdifferently than it would use had the touching not been detected.Additionally or alternatively, a user interface may provide anindication that touching the ROI hinders the detection of thephysiological response.

The computer detects the physiological response based on TH_(ROI) and M.Optionally, since the computer utilizes M to account, at least in part,for the effect of touching the face, on average, detections of thephysiological response based on TH_(ROI) and M are more accurate thandetections of the physiological response based on TH_(ROI) without M.The computer may utilize TH_(ROI) in various ways in order to detect thephysiological response, such as comparing one or more values derivedfrom TH_(ROI) to a threshold and/or comparing TH_(ROI) to a referencetime series.

Another approach that may be utilized involves a machine learning-basedmodel. In one embodiment, the computer generates feature values based onTH_(ROI) and M, and utilizes the model to detect, based on the featurevalues, the physiological response. By utilizing M to generate one ormore of the feature values, the computer may make different detectionsof the physiological response based on similar TH_(ROI) that are takenwhile there are different extents of touching the ROI. For example,responsive to receiving a first set of measurements in which TH_(ROI)reaches a threshold, while M indicate that there was no touching of theROI, the computer detects the physiological response. However,responsive to receiving a second set of measurements in which TH_(ROI)reaches the threshold, but M indicate that the user touched the ROI, thecomputer does not detect the physiological response. Optionally, themodel is trained based on samples, each comprising: (i) feature valuesgenerated based on TH_(ROI) taken while M indicates touching the ROI,and (ii) a corresponding label indicative of an extent of thephysiological response. Optionally, the samples include: a first set ofsamples with labels corresponding to having the physiological response,which are generated based on M indicating that the ROI was not touched,and a second set of samples with labels corresponding to not having thephysiological response, which are generated based on M indicating thatthe ROI was touched. Optionally, the samples comprise: a third set ofsamples with labels corresponding to having the physiological response,which are generated based on M indicating that the ROI was touched,and/or a fourth set of samples with labels corresponding to not havingthe physiological response, which are generated based on M indicatingthat the ROI was not touched.

M may be utilized by the computer in order to decrease the chance ofmaking incorrect detections of the physiological response. In oneembodiment, the computer utilizes, for the detection of thephysiological response, TH_(ROI) taken at times in which M are notindicative of touching the ROI. In this embodiment, the computer doesnot utilize, for the detection of the physiological response, TH_(ROI)taken at times in which M are indicative of touching the ROI. In anotherembodiment, the computer does not utilize, for the detection of thephysiological response, TH_(ROI) taken during at least one of thefollowing intervals starting after M indicate that the user touched theROI: 0-10 seconds, 0-30 second, 0-60 second, 0-180 seconds, and 0-300seconds. In yet another embodiment, the computer attributes, for thedetection of the physiological response, a smaller weight to TH_(ROI)taken during a certain interval starting after M indicate that the usertouched the ROI, compared to a weight attributed to TH_(ROI) taken attimes shortly before M indicate that the user touched the ROI.Optionally, the certain interval includes at least one of the followingdurations: 10-30 second, 30-60 second, 60-120 seconds, and 120-300seconds. Optionally, the higher the weight attributed to a measurement,the more it influences calculations involved in the detection of thephysiological response.

In one embodiment, the system optionally includes a user interface (UI)which notifies the user about touching the ROI. Optionally, thisnotification is in lieu of notifying extent of the physiologicalresponse corresponding to the time the user touched the ROI. Thenotification may be delivered to the user using a sound, a visualindication on a head-mounted display, and/or a haptic feedback.Optionally, the UI includes a screen of an HMS (e.g., a screen of anaugmented reality headset), a screen of a device carried by the user(e.g., a screen of a smartphone or a smartwatch), and/or a speaker(e.g., an earbud or headphones). Optionally, the computer identifiesthat the duration and/or extent of touching the face reached athreshold, and then commands the UI to alert the user that an accuratedetection of the physiological response cannot be made as long as thetouching continues.

In one embodiment, the sensor includes a visible-light camera and/or anear-infrared camera, the system is powered by a battery, and the systemmay operate in a state belonging to a set comprising first and secondstates. While operating in the first state, the system checks on aregular basis whether the user touches the ROI. While operating in thesecond state, the system checks whether the user touches the ROI inresponse to detecting abnormal TH_(ROI). Optionally, the system consumesless power while operating in the second state compared to the power itconsumes while operating in the first state.

In one embodiment, the measurements taken by the sensor are furtherindicative of an angular position of CAM relative to the ROI while theframe is still worn on the head, and the computer detects thephysiological response also based on the angular position. Optionally,the measurements of the angular position are utilized to account forinstances in which the frame has moved, and consequently CAM captures aregion that only overlaps, or does not overlap at all, with the intendedROI. Optionally, the computer is able to detect changes below 5° in theangular position, which may also influence TH_(ROI). Thus, on average,detections of the physiological response based on TH_(ROI) and theangular position are more accurate compared to detections of thephysiological responses based on TH_(ROI) without the angular position,while the frame is still worn on the head.

In a first example, responsive to the angular position of CAM relativeto the ROI reaching a predetermined threshold, the computer refrainsfrom detecting the physiological response and/or alerts the user.

In a second example, the computer generates feature values based onTH_(ROI) and the angular position, and utilizes a model to detect thephysiological response based on the feature values. Optionally, themodel was trained based on data comprising TH_(ROI) collected while CAMwas at different distances and/or angular positions relative to the ROI.Thus, the model may account, in its parameters, for various effects thatthe distance and/or orientation of CAM may have on TH_(RO) in order tomore accurately detect the physiological response.

In a third example, the sensor includes a visible-light camera thattakes images of a region on the user's face, and the computer calculatesthe angular position of the visible-light camera relative to the facebased on analyzing the images, and then calculates the angular positionof CAM relative to the ROI based on a predetermined transformationbetween the angular position of the visible-light camera relative to theface and the angular position of CAM relative to the ROI.

In a fourth example, the sensor includes a transceiver ofelectromagnetic waves, and the computer calculates the angular positionof the transceiver relative to the face based on signal processing ofthe reflections from the face, and then calculates the angular positionof CAM relative to the ROI based on a predetermined transformationbetween the angular position of the transceiver relative to the face andthe angular position of CAM relative to the ROI.

The following method for detecting a physiological response may be used,in some embodiments, by the system described above, which detects aphysiological response while taking into account a confounding factorsuch as touching the face. The steps described below may be performed byrunning a computer program having instructions for implementing themethod. Optionally, the instructions may be stored on acomputer-readable medium, which may optionally be a non-transitorycomputer-readable medium. In response to execution by a system includinga processor and memory, the instructions cause the system to perform thefollowing steps: In Step 1, taking thermal measurements of an ROI(TH_(ROI)) on a user's face using an inward-facing head-mounted thermalcamera. In Step 2, taking, utilizing a sensor, measurements (M)indicative of times at which the user touches the ROI. Touching the ROImay affect TH_(ROI), for example by increasing the temperatures at theROI (which may increase the values of TH_(ROI)). The sensor may be ahead-mounted sensor or a sensor that is not head-mounted. And in Step 3,detecting the physiological response based on TH_(ROI) and M. Forexample, the detection may be performed by the computer, as describedabove. On average, detections of the physiological response based onTH_(ROI) and M are more accurate compared to detections of thephysiological response based on TH_(ROI) without M.

Optionally, the method further includes the following steps: generatingfeature values based on TH_(ROI) and M, and utilizing a model fordetecting the physiological response based on the feature values.Optionally, the model was trained based on samples, each comprising: (i)feature values generated based on previous TH_(ROI) taken while Mindicates touching the ROI, and (ii) a corresponding label indicative ofan extent of the physiological response. Optionally, the samplesinclude: a first set of samples with labels corresponding to having thephysiological response, which are generated based on M indicating thatthe ROI was not touched, and a second set of samples with labelscorresponding to not having the physiological response, which aregenerated based on M indicating that the ROI was touched.

Optionally, M are further indicative of angular position of CAM relativeto the ROI, while the frame is still worn on the head. And the methodfurther includes a step of detecting the physiological response alsobased on the angular position. On average, detections of thephysiological response based on TH_(ROI) and the angular position aremore accurate compared to detections of the physiological responsesbased on TH_(ROI) without the angular position, while the frame is stillworn on the head.

The following is a description of a system that detects a physiologicalresponse while taking into account a consumption of a confoundingsubstance. When a person consumes a confounding substance, it may affectthermal measurements of an ROI (TH_(ROI)) on the person's face. Theaffect to TH_(ROI) can be attributed to various physiological and/ormetabolic processes that may ensue following the consumption of theconfounding substance, which can result (amongst possibly other effects)in a raising or decreasing of the temperature at the ROI in a mannerthat is unrelated to the physiological response being detected. Thus,embodiments of this system utilize indications indicative of consumptionof a confounding substance (such as medication, an alcoholic beverage, acaffeinated beverage, and/or a cigarette) to improve the system'sdetection accuracy. In one embodiment, the system includes a CAM and acomputer.

CAM is worn on the user's head and takes thermal measurements of an ROI(TH_(ROI)) on the user's face. Optionally, the system includes a frameto which CAM and the device are physically coupled. Optionally, CAM islocated less than 15 cm from the face and/or weighs below 10 g.

In different embodiments, the ROI may cover different regions on theface and CAM may be located at different locations on a frame worn onthe user's head and/or at different distances from the user's face. Inone embodiment, the ROI is on the forehead, and CAM is physicallycoupled to an eyeglasses frame, located below the ROI, and does notocclude the ROI. Optionally, the physiological response detected in thisembodiment is stress, a headache, and/or a stroke. In anotherembodiment, the ROI is on the periorbital area, and CAM is located lessthan 10 cm from the ROI. Optionally, the physiological response detectedin this embodiment is stress. In yet another embodiment, the ROI is onthe nose, and CAM is physically coupled to an eyeglasses frame and islocated less than 10 cm from the face. Optionally, the physiologicalresponse detected in this embodiment is an allergic reaction. In stillanother embodiment, the ROI is below the nostrils, and CAM: isphysically coupled to an eyeglasses frame, located above the ROI, anddoes not occlude the ROI. Optionally, the ROI covers one or more areason the upper lip, the mouth, and/or air volume(s) through which theexhale streams from the nose and/or mouth flow, and the physiologicalresponse detected in this embodiment is a respiratory parameter such asthe user's breathing rate.

The computer may receive, from a device, an indication indicative ofconsuming a confounding substance that is expected to affects TH_(ROI),such as an alcoholic beverage, a medication, caffeine, and/or acigarette. Various types of devices may be utilized in differentembodiments in order to identify consumption of various confoundingsubstances.

In one embodiment, the device includes a visible-light camera that takesimages of the user and/or the user's environment. Optionally, thevisible-light camera is a head-mounted visible-light camera having inits field of view a volume that protrudes out of the user's mouth.Optionally, the computer identifies a consumption of the confoundingsubstance based on analyzing the images. In one example, thevisible-light camera may belong to a camera-based system such as OrCam(http://www.orcam.com/), which is utilized to identify various objects,products, faces, and/or recognize text. In another example, imagescaptured by the visible-light camera may be utilized to determine thenutritional composition of food a user consumes. Such an approach inwhich images of meals are utilized to generate estimates of food intakeand meal composition, is described in Noronha, et al., “Platemate:crowdsourcing nutritional analysis from food photographs”, Proceedingsof the 24th annual ACM symposium on User interface software andtechnology, ACM, 2011. Additional examples of how a visible-light cameramay be utilized to identify consumption of various substances is givenin U.S. Pat. No. 9,053,483 (Personal audio/visual system providingallergy awareness) and in U.S. Pat. No. 9,189,021 (Wearable foodnutrition feedback system).

In another embodiment, the device includes a microphone that records theuser, and the computer identifies a consumption of the confoundingsubstance utilizing a sound recognition algorithm operated on arecording of the user. Optionally, the sound recognition algorithmcomprises a speech recognition algorithm configured to identify wordsthat are indicative of consuming the confounding substance.

In yet another embodiment, the confounding substance is a medication,and the device includes a pill dispenser that provides an indicationindicating that the user took a medication, and/or a sensor-enabled pillthat includes an ingestible signal generator coupled to a medicationthat is configured to generate a body-transmissible signal uponingestion by a user, such as the sensor-enabled pill described in PCTpublication WO/2016/129286. Optionally, the indication indicates thetype of medication and/or its dosage.

In still another embodiment, the device is a refrigerator, a pantry,and/or a serving robot. Optionally, the device provides an indicationindicative of the user taking an alcoholic beverage and/or a food item.

In yet another embodiment, the device has an internet-of-things (IoT)capability through which the indication is provided to the system. Forexample, the device may be part of a “smart device” with networkconnectivity.

And in yet another embodiment, the device belongs to a user interfacethat receives an indication from the user or/or a third party about theconsuming of the confounding substance.

Due to various metabolic and/or other physiological processes,consumption of a confounding substance may affect TH_(ROI). For example,many drugs are known to act on the hypothalamus and other brain centersinvolved in controlling the body's thermoregulatory system. Herein,stating “the confounding substance affects TH_(ROI)” means thatconsuming the confounding substance leads to a measurable change of thetemperature at the ROI, which would likely not have occurred had theconfounding substance not been consumed. Similarly, a time in which“confounding substance did not affect TH_(ROI)” is a time that occursafter at least a certain duration has elapsed since the confoundingsubstance was last consumed (or was not consumed at all), and theconsumption of the confounding substance is no longer expected to have anoticeable effect on the ROI temperature. This certain duration maydepend on factors such as the type of substance, the amount consumed,and previous consumption patterns. For example, the certain duration maybe at least: 30 minutes, two hours, or a day.

The duration of the effect of a confounding substance may vary betweensubstances, and may depend on various factors such as the amount ofsubstance, sex, weight, genetic characteristics, and the user's state.For example, consumption of alcohol on an empty stomach often has agreater effect on TH_(ROI) than consumption of alcohol with a meal. Someconfounding substances may have a long-lasting effect, possiblythroughout the period they are taken. For example, hormonalcontraceptives can significantly alter daily body temperatures. Otherconfounding factors, such as caffeine and nicotine, may have shorterlasting effects, typically subsiding within less than an hour or twofollowing their consumption.

The computer detects the physiological response based on TH_(ROI) andthe indication indicative of consuming the confounding substance. In oneembodiment, the computer refrains from detecting the physiologicalresponse within a certain window during which the confounding substanceaffected the user (e.g., an hour, two hours, or four hours). In anotherembodiment, the computer utilizes a model, in addition to TH_(ROI) andthe indication, to detect whether the user had the physiologicalresponse during the time that a consumed confounding substance affectedTH_(ROI). Optionally, the computer detects the physiological response bygenerating feature values based on TH_(ROI) and the indication (andpossibly other sources of data), and utilizing the model to calculate,based on the feature values, a value indicative of the extent of thephysiological response. Optionally, the feature values include a featurevalue indicative of one or more of the following: the amount of theconsumed confounding substance, the dosage of the consumed confoundingsubstance, the time that has elapsed since the confounding substance hadlast been consumed, and/or the duration during which the confoundingfactor has been consumed (e.g., how long the user has been taking acertain medication).

In one embodiment, the model was trained based on data collected fromthe user and/or other users, which includes TH_(ROI), the indicationsdescribed above, and values representing the physiological responsecorresponding to when TH_(ROI) were taken. Optionally, the data is usedto generate samples, with each sample comprising feature values and alabel. The feature values of each sample are generated based on TH_(ROI)taken during a certain period and an indication indicating whether aconfounding substance affected TH_(ROI) taken during the certain period.The label of the sample is generated based on one or more of the valuesrepresenting the physiological response, and indicates whether (andoptionally to what extent) the measured user had the physiologicalresponse during the certain period. Optionally, the data used to trainthe model reflects both being affected and being unaffected by theconfounding substance. For example, the data used to train the model mayinclude: a first set of TH_(ROI) taken while the confounding substanceaffected TH_(ROI), and a second set of TH_(ROI) taken while theconfounding substance did not affect TH_(ROI). Optionally, each of thefirst and second sets comprises at least some TH_(ROI) taken while themeasured user had the physiological response and at least some TH_(ROI)taken while the measured user did not have the physiological response.

Using the indications (indicative of the user consuming a confoundingsubstance) may lead to cases where the detection of the physiologicalresponse depends on whether the confounding substance was consumed. Inone example, in which the physiological response is detected whenTH_(ROI) reach a threshold, the computer's detection behavior may be asfollows: the computer detects the physiological response based on firstTH_(ROI) for which there is no indication indicating that the firstTH_(ROI) were affected by a consumption of the confounding substance,and the first TH_(ROI) reach the threshold; the computer does not detectthe physiological response based on second TH_(ROI) for which there isan indication indicating that the second TH_(ROI) were affected by aconsumption of the confounding substance, and the second TH_(ROI) alsoreach the threshold; and the computer does not detect the physiologicalresponse based on third TH_(ROI) for which there is no indicationindicating that the third TH_(ROI) were affected by a consumption theconfounding substance, and the third TH_(ROI) do not reach thethreshold.

The following three figures illustrate scenarios where issuing of alertsare dependent on the consumption of confounding substances. FIG. 54illustrates that the effect of consuming alcohol on a certain TH_(ROI)usually decreases after duration typical to the user (e.g., the durationis based on previous measurements of the user). Thus, when the effectremains high there may be a problem and the system may issue an alert.The figure illustrates an outward-facing visible-light camera 525 thatgenerates the indications indicative of when the user consumes alcoholicbeverages.

FIG. 55 illustrates a usual increase in a certain TH_(ROI) while theuser smokes. The system identifies when the user smoked (e.g., based onimages taken by the outward-facing visible-light camera 525) and doesn'talert because of an increase in TH_(ROI) caused by the smoking. However,when the temperature rises without the user having smoked for a certaintime, then it may be a sign that there is a problem, and the user mightneed to be alerted.

FIG. 56 illustrates the expected decrease in a certain TH_(ROI) afterthe user takes medication, based on previous TH_(ROI) of the user. Thesystem identifies when the medication is consumed, and does not generatean alert at those times. However, when TH_(ROI) falls without medicationhaving been taken, it may indicate a physiological response of which theuser should be made aware.

The following method for detecting a physiological response while takinginto account consumption of a confounding substance may be used, in someembodiments, by the system described above, which detects aphysiological response while taking into account a consumption of aconfounding substance. The steps described below may be performed byrunning a computer program having instructions for implementing themethod. Optionally, the instructions may be stored on acomputer-readable medium, which may optionally be a non-transitorycomputer-readable medium. In response to execution by a system includinga processor and memory, the instructions cause the system to perform thefollowing steps:

In Step 1, taking thermal measurements of an ROI (TH_(ROI)) on theuser's face utilizing an inward-facing head-mounted thermal camera.

In Step 2, receiving an indication indicative of consuming a confoundingsubstance that affects TH_(ROI). Optionally, the indication is receivedfrom one or more of the various device described above that provide anindication indicative of consuming a confounding substance. Optionally,the indication is generated based on image processing of images taken bya head-mounted visible-light camera having in its field of a volume thatprotrudes out of the user's mouth.

And in Step 3, detecting the physiological response, while the consumedconfounding substance affects TH_(ROI), based on TH_(ROI), theindication, and a model. Optionally, the model was trained on: a firstset of TH_(ROI) taken while the confounding substance affected TH_(ROI),and a second set of TH_(ROI) taken while the confounding substance didnot affect TH_(ROI). Optionally, the model is a machine learning-basedmodel, and this step involves: generating feature values based onTH_(ROI) and the indication, and utilizing the machine learning-basedmodel to detect the physiological response based on the feature values.

One way in which a user may wear a head-mounted camera (such as CAM orVCAM) involves attaching a clip-on device that houses the camera onto aframe worn by the user, such as an eyeglasses frame. This may enable theuser to be selective regarding when to use the head-mounted camera andtake advantage of eyeglasses that he or she owns, which may becomfortable and/or esthetically pleasing.

In some embodiments, the clip-on device includes a body that may beattached and detached, multiple times, from a pair of eyeglasses inorder to secure and release the clip-on device from the eyeglasses. Thebody is a structure that has one or more components fixed to it. Forexample, the body may have one or more inward-facing camera fixed to it.Additionally, the body may have a wireless communication module fixed toit. Some additional components that may each be optionally fixed to thebody include a processor, a battery, and one or more outward-facingcameras.

In one example, “eyeglasses” are limited to prescription eyeglasses,prescription sunglasses, plano sunglasses, and/or augmented realityeyeglasses. This means that “eyeglasses” do not refer to helmets, hats,virtual reality devices, and goggles designed to be worn overeyeglasses. Additionally or alternatively, neither attaching the clip-ondevice to the eyeglasses nor detaching the clip-on device from theeyeglasses should take more than 10 seconds for an average user. Thismeans that manipulating the clip-on device is not a complicated task.Optionally, the body is configured to be detached from the eyeglasses bythe user who wears the eyeglasses, who is not a technician, and withoutusing a tool such as a screwdriver or a knife. Thus, the clip-on devicemay be attached and detached as needed, e.g., enabling the user toattach the clip-on when there is a need to take measurements, andotherwise have it detached.

In order to be warn comfortably, possibly for long durations, theclip-on device is a lightweight device, weighing less than 40 g (i.e.,the total weight of the body and the components fixed to it is less than40 g). Optionally, the clip-on device weighs below 20 g and/or below 10g.

The body is a structure to which components (e.g., an inward-facingcamera) may be fixed such that the various components do not fall offwhile the clip-on device is attached to the eyeglasses. Optionally, atleast some of the various components that are fixed to the body remainin the same location and/or orientation when the body is attached to theeyeglasses. Herein, stating that a component is “fixed” to the body isintended to indicate that, during normal use (e.g., involvingsecuring/releasing the clip-on device), the components are typically notdetached from the body. This is opposed to the body itself, which innormal use is separated from the eyeglasses frame, and as such, is notconsidered “fixed” to the eyeglasses frame.

In some embodiments, the body is a rigid structure made of a materialsuch as plastic, metal, and/or an alloy (e.g., carbon alloy).Optionally, the rigid structure is shaped such that it fits the contoursof at least a portion of the frame of the eyeglasses in order to enablea secure and stable attachment to the eyeglasses. In other embodiments,the body may be made of a flexible material, such as rubber. Optionally,the flexible body is shaped such that it fits the contours of at least aportion of the frame of the eyeglasses in order to enable a secure andstable attachment to the eyeglasses. Additionally or alternatively, theflexible body may assume the shape of a portion of the frame when it isattached to the eyeglasses.

The body may utilize various mechanisms in order to stay attached to theeyeglasses. In one embodiment, the body may include a clip memberconfigured to being clipped on the eyeglasses. In another embodiment,the body may include a magnet configured to attach to a magnet connectedto the eyeglasses and/or to a metallic portion of the eyeglasses. In yetanother embodiment, the body may include a resting tab configured tosecure the clip-on to the eyeglasses. In still another embodiment, thebody may include a retention member (e.g., a clasp, buckle, clamp,fastener, hook, or latch) configured to impermanently couple the clip-onto the eyeglasses. For example, clasp 147 is utilized to secure theclip-on device illustrated in FIG. 17a to the frame of the eyeglasses.And in yet another embodiment, the body may include a spring configuredto apply force that presses the body towards the eyeglasses. An exampleof this type of mechanism is illustrated in FIG. 19a where spring 175 isused to apply force that pushes body 170 and secures it in place toframe 176.

Herein, to “impermanently couple” something means to attach in a waythat is easily detached without excessive effort. For example, couplingsomething by clipping it on or closing a latch is consideredimpermanently coupling it. Coupling by screwing a screw with ascrewdriver, gluing, or welding is not considered impermanentlycoupling. The latter would be examples of what may be considered to“fix” a component to the body.

The inward-facing camera is fixed to the body. It takes images of aregion of interest on the face of a user who wears the eyeglasses.Optionally, the inward-facing camera remains pointed at the region ofinterest even when the user's head makes lateral and/or angularmovements. The inward-facing camera may be any of the CAMs and/or VCAMsdescribed in this disclosure. Optionally, the inward-facing cameraweighs less than 10 g, 5 g or 1 g. Optionally, the inward-facing camerais a thermal camera based on a thermopile sensor, a pyroelectric sensor,or a microbolometer sensor, which may be a FPA sensor.

In one embodiment, the inward-facing camera includes a multi-pixelsensor and a lens, and the sensor plane is tilted by more than 2°relative to the lens plane according to the Scheimpflug principle inorder to capture sharper images when the body is attached to theeyeglasses that are worn by a user.

The clip-one device may include additional components that are fixed toit. In one embodiment, the clip-on device include a wirelesscommunication module fixed to the body which transmits measurements(e.g., images and/or thermal measurements) taken by one or more of thecameras that are fixed to the body. Optionally, the clip-on device mayinclude a battery fixed to the body, which provides power to one or morecomponents fixed to the body. Optionally, the clip-on device may includea processor that controls the operation of one or more of the componentsfixed to the body and/or processes measurements taken by the camerafixed to the body.

In some embodiments, a computer receives measurements taken by theinward-facing camera (and possibly other cameras fixed to the body), andutilizes the measurements to detect a physiological response.Optionally, the computer is not fixed to the body. For example, thecomputer may belong to a device of the user (e.g., a smartphone or asmartwatch), or the computer may be a cloud-based server. Optionally,the computer receives, over a wireless channel, the measurements, whichare sent by the wireless communication module.

The following are various examples of embodiments using different typesof inward- and outward-facing cameras that are fixed to the body, whichmay be used to take images of various regions of interest on the face ofthe user who wears the eyeglasses. It is to be noted that while thediscussion below generally refers to a single “inward-facing camera”and/or a single “outward-facing camera”, embodiments of the clip-ondevice may include multiple inward- and/or outward-facing cameras.

In some embodiments, the inward-facing camera is a thermal camera.Optionally, when the body is attached to the eyeglasses, the thermalcamera is located less than 5 cm from the user's face. Optionally,measurements taken by the thermal camera are transmitted by the wirelesscommunication module and are received by a computer that uses them todetect a physiological response of the user. In one example, when thebody is attached to the eyeglasses, the optical axis of the thermalcamera is above 20° from the Frankfort horizontal plane, and the thermalcamera takes thermal measurements of a region on the user's forehead. Inanother example, when the body is attached to the eyeglasses, thethermal camera takes thermal measurements of a region on the user'snose. In yet another example, when the body is attached to theeyeglasses, the thermal camera takes thermal measurements of a region ona periorbital area of the user.

In one embodiment, the inward-facing camera is a thermal camera. Whenthe body is attached to the eyeglasses, the thermal camera is locatedbelow eye-level of a user who wears the eyeglasses and at least 2 cmfrom the vertical symmetry axis that divides the user's face (i.e., theaxis the goes down the center of the user's forehead and nose).Additionally, when the body is attached to the eyeglasses, theinward-facing thermal camera takes thermal measurements of a region onat least one of the following parts of the user's face: upper lip, lips,and a cheek. Optionally, measurements taken by the thermal camera aretransmitted by the wireless communication module and are received by acomputer that uses them to detect a physiological response of the user.

In another embodiment, the inward-facing camera is a visible-lightcamera. Optionally, when the body is attached to the eyeglasses, thevisible-light camera is located less than 10 cm from the user's face.Optionally, images taken by the visible-light camera are transmitted bythe wireless communication module and are received by a computer thatuses them to detect a physiological response of the user. Optionally,the computer detects the physiological response based on facial skincolor changes (FSCC) that are recognizable in the images. In oneexample, when the body is attached to the eyeglasses, the optical axisof the visible-light camera is above 20° from the Frankfort horizontalplane, and the visible-light camera takes images of a region locatedabove the user's eyes. In another example, when the body is attached tothe eyeglasses, the visible-light camera takes images of a region on thenose of a user who wears the eyeglasses. In still another example, thecomputer detects the physiological response based on facial expressions,and when the body is attached to the eyeglasses, the visible-lightcamera takes images of a region above or below the user's eyes.

In still another embodiment, the inward-facing camera is a visible-lightcamera, and when the body is attached to the eyeglasses, thevisible-light camera takes images of a region on an eye (IM_(E)) of auser who wears the eyeglasses, and is located less than 10 cm from theuser's face. Optionally, the images are transmitted by the wirelesscommunication module and are received by a computer that detects aphysiological response based in IM_(E).

In one example, the computer detects the physiological response based oncolor changes to certain parts of the eye, such as the sclera and/or theiris. Due to the many blood vessels that are close to the surface of theeye, physiological responses that are manifested through changes to theblood flow (e.g., a cardiac pulse and certain emotional responses), maycause recognizable changes to the color of the certain parts of the eye.The various techniques described in this disclosure for detecting aphysiological response based on FSCC that is recognizable in images canbe applied by one skilled in the art to detect a physiological responsebased on color changes to the sclera and/or iris; while the sclera andiris are not the same color as a person's skin, they too exhibit bloodflow-related color changes that are qualitatively similar to FSCC, andthus may be analyzed using similar techniques to the techniques used toanalyze FSCC involving the forehead, nose, and/or cheeks.

In another example, IM_(E) may be utilized to determine the size of thepupil, which may be utilized by the computer to detect certain emotionalresponses (such as based on the assumption that the pupil's responsereflects emotional arousal associated with increased sympatheticactivity).

If needed as part of the computer's detection of the physiologicalresponse, identifying which portions of IM_(E) correspond to certainparts of the eye (e.g., the sclera or iris) can be done utilizingvarious image processing techniques known in the art. For example,identifying the iris and pupil size may be done using the techniquesdescribed in US patent application 20060147094, or in Hayes, Taylor R.,and Alexander A. Petrov. “Mapping and correcting the influence of gazeposition on pupil size measurements.” Behavior Research Methods 48.2(2016): 510-527. Additionally, due to the distinct color differencesbetween the skin, the iris, and the sclera, identification of the irisand/or the white sclera can be easily done by image processing methodsknown in the art.

In one embodiment, the inward-facing camera is a visible-light camera;when the body is attached to the eyeglasses, the visible-light camera islocated below eye-level of a user who wears the eyeglasses, and at least2 cm from the vertical symmetry axis that divides the user's face. Thevisible-light camera takes images (IM_(ROI)) of a region on the upperlip, lips, and/or a cheek. Optionally, IM_(ROI) are transmitted by thewireless communication module and are received by a computer that usesthem to detect a physiological response. In one example, thephysiological response is an emotional response, which is detected basedon extracting facial expressions from IM_(ROI). In another example, thephysiological response is an emotional response, which is detected basedon FSCC recognizable in IM_(ROI). In still another example, thephysiological response, which is detected based FSCC recognizable inIM_(ROI), is heart rate and/or breathing rate.

The body may include an outward-facing camera that may be utilized toprovide measurements that may be used to account for variousenvironmental interferences that can decrease detections of thephysiological response of a user who wears the eyeglasses. Optionally,the outward-facing camera is a head-mounted camera. Optionally, theoutward-facing camera is fixed to the body.

In one embodiment, the inward-facing camera is a thermal camera, andwhen the body is attached to the eyeglasses, the thermal camera islocated less than 10 cm from the face of the user who wears theeyeglasses, and takes thermal measurements of a region of interest(TH_(ROI)) on the face of the user. In this embodiment, anoutward-facing head-mounted thermal camera takes thermal measurements ofthe environment (TH_(ENV)). The wireless communication module transmitsTH_(ROI) and TH_(ENV) to a computer that detects a physiologicalresponse of the user based on TH_(ROI) and TH_(ENV). Optionally, thecomputer utilizes TH_(ENV) to account for thermal interferences from theenvironment, as discussed elsewhere herein.

In another embodiment, the inward-facing camera is a visible-lightcamera, and when the body is attached to the eyeglasses, thevisible-light camera is located less than 10 cm from the face of theuser who wears the eyeglasses and takes images of a region of interest(IM_(ROI)) on the face of the user. In this embodiment, anoutward-facing head-mounted visible-light camera takes images of theenvironment (IM_(ENV)). The wireless communication module transmitsIM_(ROI) and IM_(ENV) to a computer that detects a physiologicalresponse of the user based on IM_(ROI) and IM_(ENV). Optionally, thecomputer detects the physiological response based on FSCC recognizablein IM_(ROI), and utilizes IM_(ENV) to account for variations in ambientlight, as discussed elsewhere herein.

Inward-facing cameras attached to the body may be utilized foradditional purposes, beyond detection of physiological responses. In oneembodiment, the inward-facing camera is a visible-light camera, and theclip-on device includes a second visible-light camera that is also fixedto the body. Optionally, the visible-light camera and/or the secondvisible-light camera are light field cameras. Optionally, when the bodyis attached to the eyeglasses, the first and second visible-lightcameras are located less than 10 cm from the user's face, and takeimages of a first region above eye-level and a second region on theupper lip (IM_(ROI) and IM_(ROI2), respectively). Optionally, thewireless communication module transmits IM_(ROI) and IM_(ROI2) to acomputer that generates an avatar of the user based on IM_(ROI) andIM_(ROI2). Some of the various approaches that may be utilized togenerate the avatar based on IM_(ROI) and IM_(ROI2) are described inco-pending US patent publication 2016/0360970.

Different embodiments of the clip-on device may involve devices ofvarious shapes, sizes, and/or locations of attachment to the eyeglasses.FIG. 16a to FIG. 20 illustrate some examples of clip-on devices. Whenthe body is attached to the eyeglasses, most of the clip-on device maybe located in front of the frame of the eyeglasses, as illustrated inFIG. 16b , FIG. 17b , and FIG. 20, or alternatively, most of the clip-ondevice may be located behind the frame, as illustrated in FIG. 18b andFIG. 19b . Some clip-on devices may include a single unit, such asillustrated in FIG. 17a and FIG. 19a . While other clip-on devices mayinclude multiple units (which each may optionally be considered aseparate clip-on device). Examples of multiple units being attached tothe frame are illustrated in FIG. 16b , FIG. 18b , and FIG. 20. Thefollowing is a more detailed discussion regarding embodimentsillustrated in the figures mentioned above.

FIG. 16a , FIG. 16b , and FIG. 16c illustrate two right and left clip-ondevices comprising bodies 141 and 142, respectively, which areconfigured to attached/detached from an eyeglasses frame 140. The body142 has multiple inward-facing cameras fixed to it, such as camera 143that points at a region on the lower part of the face (such as the upperlip, mouth, nose, and/or cheek), and camera 144 that points at theforehead. The body 142 may include other electronics 145, such as aprocessor, a battery, and/or a wireless communication module. The bodies141 and 142 of the left and right clip-on devices may include additionalcameras illustrated in the drawings as black circles.

In one another embodiment, the eyeglasses include left and right lenses,and when the body is attached to the eyeglasses, most of the volume ofthe clip-on device is located to the left of the left lens or to theright of the right lens. Optionally, the inward-facing camera takesimages of at least one of: a region on the nose of a user wearing theeyeglasses, and a region on the mouth of the user. Optionally, a portionof the clip-on device that is located to the left of the left lens or tothe right of the right lens does not obstruct the sight of the user whenlooking forward.

FIG. 17a and FIG. 17b illustrate a clip-on device that includes a body150, to which two head-mounted cameras are fixed: a head-mounted camera148 that points at a region on the lower part of the face (such as thenose), and a head-mounted camera 149 that points at the forehead. Theother electronics (such as a processor, a battery, and/or a wirelesscommunication module) are located inside the body 150. The clip-ondevice is attached and detached from the frame of the eyeglasses withthe clasp 147.

In one embodiment, when the body is attached to the eyeglasses, most ofthe volume of the clip-on device is located above the lenses of theeyeglasses, and the inward-facing camera takes images of a region on theforehead of a user who wears the eyeglasses. Optionally, a portion ofthe clip-on device that is located above the lenses of the eyeglassesdoes not obstruct the sight of the user when looking forward.

While the clip-on device may often have a design intended to reduce theextent to which it sticks out beyond the frame, in some embodiments, theclip-on device may include various protruding arms. Optionally, thesearms may be utilized in order to position one or more cameras in aposition suitable for taking images of certain regions of the face. FIG.20 illustrates right and left clip-on devices that include bodies 153and 154, respectively, which are configured to attached/detached from aneyeglasses frame. These bodies have protruding arms that hold thehead-mounted cameras. Head-mounted camera 155 measures a region on thelower part of the face, head-mounted camera 156 measures regions on theforehead. The left clip-on device also includes other electronics 157(such as a processor, a battery, and/or a wireless communicationmodule). The clip-on devices illustrated in this figure may includeadditional cameras illustrated in the drawings as black circles.

In other embodiments, at least a certain portion of the clip-on deviceis located behind the eyeglasses' frame. Thus, when the clip-on deviceis attached to the eyeglasses, they may remain aesthetically pleasing,and attaching the clip-on device may cause little or no blocking of theuser's vision. FIG. 18b and FIG. 19b illustrate two examples of clip-ondevices that are mostly attached behind the frame. The following aresome additional examples of embodiments in which a portion of theclip-on device may be located behind the frame.

FIG. 18a and FIG. 18b illustrate two, right and left, clip-on deviceswith bodies 160 and 161, respectively, configured to be attached behindan eyeglasses frame 165. The body 160 has various components fixed to itwhich include: an inward-facing head-mounted camera 162 pointed at aregion below eye-level (such as the upper lip, mouth, nose, and/orcheek), an inward-facing head-mounted camera 163 pointed at a regionabove eye-level (such as the forehead), and other electronics 164 (suchas a processor, a battery, and/or a wireless communication module). Theright and left clip-on devices may include additional camerasillustrated in the drawings as black circles.

FIG. 19a and FIG. 19b illustrate a single-unit clip-on device thatincludes the body 170, which is configured to be attached behind theeyeglasses frame 176. The body 170 has various cameras fixed to it, suchas head-mounted cameras 171 and 172 that are pointed at regions on thelower part of the face (such as the upper lip, mouth, nose, and/orcheek), and head-mounted cameras 173 and 174 that are pointed at theforehead. The spring 175 is configured to apply force that holds thebody 170 to the frame 176. Other electronics 177 (such as a processor, abattery, and/or a wireless communication module), may also be fixed tothe body 170. The clip-on device may include additional camerasillustrated in the drawings as black circles.

In one embodiment, when the body is attached to the eyeglasses, morethan 50% of the out-facing surface of the clip-on device is locatedbehind the eyeglasses frame. Optionally, a portion of the clip-on devicethat is located behind the eyeglasses frame is occluded from a viewerpositioned directly opposite to the eyeglasses, at the same height asthe eyeglasses. Thus, a portion of the clip-on device that is behind theframe might not be visible to other people from many angles, which canmake the clip-on device less conspicuous and/or more aestheticallypleasing. Optionally, a larger portion of the clip-on device is behindthe frame when the body is attached to the eyeglasses, such as more than75% or 90% of the out-facing surface.

Various physiological responses may be detected based on Facial skincolor changes (FSCC) that occur on a user's face. In one embodiment, asystem configured to detect a physiological response based on FSCCincludes at least an inward-facing head-mounted visible-light camera(VCAM_(in)) and a computer. The system may optionally include additionalelements such as a frame and additional inward-facing camera(s) and/oroutward-facing camera(s).

FIG. 47 illustrates one embodiment of the system configured to detect aphysiological response based on FSCC. The system includes a frame 735(e.g., an eyeglasses frame) to which various cameras are physicallycoupled. These cameras include visible-light cameras 740, 741, 742, and743, which may each take images of regions on the user's cheeks and/ornose. Each of these cameras may possibly be VCAM_(in), which isdiscussed in more detail below. Another possibility for VCAM_(in) iscamera 745 that takes images of a region on the user's forehead and iscoupled to the upper portion of the frame. Visible-light camera 737,which takes images of the environment (IM_(ENV)), is an example ofVCAM_(out) discussed below, which may optionally be included in someembodiments. Additional cameras that may optionally be included in someembodiments are outward-facing thermal camera 738 (which may be used totake TH_(ENV) mentioned below) and inward-facing thermal camera 739(which may be used to take TH_(ROI2) mentioned below).

VCAM_(in) is worn on the user's head and takes images of a region ofinterest (IM_(ROI)) on the user's face. Depending on the physiologicalresponse being detected, the ROI may cover various regions on the user'sface. In one example, the ROI is on a cheek of the user, a region on theuser's nose, and/or a region on the user's forehead. Optionally,VCAM_(in) does not occlude the ROI, is located less than 10 cm from theuser's face, and weighs below 10 g. The ROI is illuminated by ambientlight. Optionally, the system does not occlude the ROI, and the ROI isnot illuminated by a head-mounted light source. Alternatively, the ROImay be illuminated by a head-mounted light source that is weaker thanthe ambient light.

The computer detects the physiological response based on IM_(ROI) byrelying on effects of FSCC that are recognizable in IM_(ROI). Herein,sentences of the form “FSCC recognizable in IM_(ROI)” refer to effectsof FSCC that may be identified and/or utilized by the computer, whichare usually not recognized by the naked eye. The FSCC phenomenon may beutilized to detect various types of physiological responses. In oneembodiment, the physiological response that is detected may involve anexpression of emotional response of the user. For example, the computermay detect whether the user's emotional response is neutral, positive,or negative. In another example, the computer may detect an emotionalresponse that falls into a more specific category such as distress,happiness, anxiousness, sadness, frustration, intrigue, joy, disgust,anger, etc. Optionally, the expression of the emotional response mayinvolve the user making a facial expression and/or a microexpression(whose occurrence may optionally be detected based on IM_(ROI)). Inanother embodiment, detecting the physiological response involvesdetermining one or more physiological signals of the user, such as aheart rate (which may also be referred to as “cardiac pulse”), heartrate variability, and/or a breathing rate.

IM_(ROI) are images generated based on ambient light illumination thatis reflected from the user's face. Variations in the reflected ambientlight may cause FSCC that are unrelated to the physiological responsebeing detected, and thus possibly lead to errors in the detection of thephysiological response. In some embodiments, the system includes anoutward-facing head-mounted visible-light camera (VCAM_(out)), which isworn on the user's head, and takes images of the environment (IM_(ENV)).Optionally, VCAM_(out) is located less than 10 cm from the user's faceand weighs below 10 g. Optionally, VCAM_(out) may include optics thatprovide it with a wide field of view. Optionally, the computer detectsthe physiological response based on both IM_(ROI) and IM_(ENV). Giventhat IM_(ENV) is indicative of illumination towards the face andIM_(ROI) is indicative of reflections from the face, utilizing IM_(ENV)in the detection of the physiological response can account, at least inpart, for variations in ambient light that, when left unaccounted, maypossibly lead to errors in detection of the physiological response.

It is noted that the system may include multiple VCAM_(in) configured totake images of various ROIs on the face, IM_(ROI) may include imagestaken from the multiple VCAM_(in), and multiple VCAM_(out) located atdifferent locations and/or orientation relative to the face may be usedto take images of the environment.

In some embodiments, VCAM_(in) and/or VCAM_(out) are physically coupledto a frame, such as an eyeglasses frame or an augmented realty deviceframe. Optionally, the angle between the optical axes of VCAM_(in) andVCAM_(out) is known to the computer, and may be utilized in thedetection of the physiological response. Optionally, the angle betweenthe optical axes of VCAM_(in) and VCAM_(out) is fixed.

Due to the proximity of VCAM_(in) to the face, in some embodiments,there may be an acute angle between the optical axis of VCAM_(in) andthe ROI (e.g., when the ROI includes a region on the forehead). In orderto improve the sharpness of IM_(ROI), VCAM_(in) may be configured tooperate in a way that takes advantage of the Scheimpflug principle. Inone embodiment, VCAM_(in) includes a sensor and a lens; the sensor planeis tilted by a fixed angle greater than 2° relative to the lens planeaccording to the Scheimpflug principle in order to capture a sharperimage when VCAM_(in) is worn by the user (where the lens plane refers toa plane that is perpendicular to the optical axis of the lens, which mayinclude one or more lenses). Optionally, VCAM_(in) does not occlude theROI. In another embodiment, VCAM_(in) includes a sensor, a lens, and amotor; the motor tilts the lens relative to the sensor according to theScheimpflug principle. The tilt improves the sharpness of IM_(ROI) whenVCAM_(in) is worn by the user.

In addition to capturing images in the visible spectrum, someembodiments may involve capturing light in the near infrared spectrum(NIR). In some embodiments, VCAM_(in) and/or VCAM_(out) may includeoptics and sensors that capture light rays in at least one of thefollowing NIR spectrum intervals: 700-800 nm, 700-900 nm, 700-1,000 nm.Optionally, the computer may utilize data obtained in a NIR spectruminterval to detect the physiological response (in addition to or insteadof data obtained from the visible spectrum). Optionally, the sensors maybe CCD sensors designed to be sensitive in the NIR spectrum and/or CMOSsensors designed to be sensitive in the NIR spectrum.

One advantage of having VCAM_(in) coupled to the frame involves thehandling of chromatic aberrations. Chromatic aberrations refractdifferent wavelengths of light at different angles, depending on theincident angle. When VCAM_(in) is physically coupled to the frame, theangle between VCAM_(in) and the ROI is known, and thus the computer maybe able to select certain subsets of pixels, which are expected tomeasure light of certain wavelengths from the ROI. In one embodiment,VCAM_(in) includes a lens and a sensor comprising pixels; the lensgenerates chromatic aberrations that refract red and blue light rays indifferent angles; the computer selects, based on the angle between thecamera and the ROI (when the user wears the frame), a first subset ofpixels to measure the blue light rays reflected from the ROI, and asecond subset of pixels to measure the red light rays reflected from theROI. Optionally, the first and second subsets are not the same.Optionally, VCAM_(in) may include a sensor that captures light rays alsoin a portion of the NIR spectrum, and the computer selects, based on theangle between VCAM_(in) and the ROI, a third subset of pixels to measurethe NIR light rays reflected from the ROI. Optionally, the second andthird subsets are not the same.

The computer may utilize various approaches in order to detect thephysiological response based on IM_(ROI). Some examples of how such adetection may be implemented are provided in the prior art referencesmentioned above, which rely on FSCC to detect the physiologicalresponse. It is to be noted that while the prior art approaches involveanalysis of video obtained from cameras that are not head-mounted, aretypically more distant from the ROI than VCAM_(in), and are possibly atdifferent orientations relative to the ROI, the computational approachesdescribed in the prior art used to detect physiological responses can bereadily adapted by one skilled in the art to handle IM_(ROI). In somecases, embodiments described herein may provide video in which a desiredsignal is more easily detectable compared to some of the prior artapproaches. For example, given the short distance from VCAM_(in) to theROI, the ROI is expected to cover a larger portion of the images inIM_(ROI) compared to images obtained by video cameras in some of theprior art references. Additionally, due to the proximity of VCAM_(in) tothe ROI, additional illumination that is required in some prior artapproaches, such as illuminating the skin for a pulse oximeter to obtaina photoplethysmographic (PPG) signal, may not be needed. Furthermore,given VCAM_(in)'s fixed location and orientation relative to the ROI(even when the user makes lateral and/or angular movements), manypre-processing steps that need to be implemented by the prior artapproaches, such as image registration and/or face tracking, areextremely simplified in embodiments described herein, or may be foregonealtogether.

IM_(ROI) may undergo various preprocessing steps prior to being used bythe computer to detect the physiological response and/or as part of theprocess of the detection of the physiological response. Somenon-limiting examples of the preprocessing include: normalization ofpixel intensities (e.g., to obtain a zero-mean unit variance time seriessignal), and conditioning a time series signal by constructing a squarewave, a sine wave, or a user defined shape, such as that obtained froman ECG signal or a PPG signal as described in U.S. Pat. No. 8,617,081.Additionally or alternatively, some embodiments may involve generatingfeature values based on a single image or a sequence of images. In someexamples, generation of feature values from one or more images mayinvolve utilization of some of the various approaches described in thisdisclosure for generation of high-level and/or low-level image-basedfeatures.

The following is a discussion of some approaches that may be utilized bythe computer to detect the physiological response based on IM_(ROI).Additionally, implementation-related details may be found in theprovided references and the references cited therein. Optionally,IM_(ENV) may also be utilized by the computer to detect thephysiological response (in addition to IM_(ROI)), as explained in moredetail below.

In some embodiments, the physiological response may be detected usingsignal processing and/or analytical approaches. Optionally, theseapproaches may be used for detecting repetitive physiological signals(e.g., a heart rate, heart rate variability, or a breathing rate) inIM_(ROI) taken during a certain period. Optionally, the detectedphysiological response represents the value of the physiological signalof the user during the certain period.

In one example, U.S. Pat. No. 8,768,438, titled “Determining cardiacarrhythmia from a video of a subject being monitored for cardiacfunction”, describes how a heart rate may be determined based on FSCC,which are represented in a PPG signal obtained from video of the user.In this example, a time series signal is generated from video images ofa subject's exposed skin, and a reference signal is used to perform aconstrained source separation (which is a variant of ICA) on the timeseries signals to obtain the PPG signal. Peak-to-peak pulse points aredetected in the PPG signal, which may be analyzed to determineparameters such as heart rate, heart rate variability, and/or to obtainpeak-to-peak pulse dynamics that can be indicative of conditions such ascardiac arrhythmia.

In another example, U.S. Pat. No. 8,977,347, titled “Video-basedestimation of heart rate variability”, describes how a times-seriessignal similar to the one described above may be subjected to adifferent type of analysis to detect the heart rate variability. In thisexample, the time series data are de-trended to remove slownon-stationary trends from the signal and filtered (e.g., using bandpassfiltering). Following that, low frequency and high frequency componentsof the integrated power spectrum within the time series signal areextracted using Fast Fourier Transform (FFT). A ratio of the low andhigh frequency of the integrated power spectrum within these componentsis computed. And analysis of the dynamics of this ratio over time isused to estimate heart rate variability.

In yet another example, U.S. Pat. No. 9,020,185, titled “Systems andmethods for non-contact heart rate sensing”, describes how atimes-series signals obtained from video of a user can be filtered andprocessed to separate an underlying pulsing signal by, for example,using an ICA algorithm. The separated pulsing signal from the algorithmcan be transformed into frequency spacing data using FFT, in which theheart rate can be extracted or estimated.

In some embodiments, the physiological response may be detected usingmachine learning-based methods. Optionally, these approaches may be usedfor detecting expressions of emotions and/or values of physiologicalsignals.

Generally, machine learning-based approaches involve training a model onsamples, with each sample including: feature values generated based onIM_(ROI) taken during a certain period, and a label indicative of thephysiological response during the certain period. Optionally, the modelmay be personalized for a user by training the model on samplesincluding: feature values generated based on IM_(ROI) of the user, andcorresponding labels indicative of the user's respective physiologicalresponses. Some of the feature values in a sample may be generated basedon other sources of data (besides IM_(ROI)), such as measurements of theuser generated using thermal cameras, movement sensors, and/or otherphysiological sensors, and/or measurements of the environment.Optionally, IM_(ROI) of the user taken during an earlier period mayserve as a baseline to which to compare. Optionally, some of the featurevalues may include indications of confounding factors, which may affectFSCC, but are unrelated to the physiological response being detected.Some examples of confounding factors include touching the face, thermalradiation directed at the face, and consuming certain substances such asa medication, alcohol, caffeine, or nicotine.

Training the model may involve utilization of various trainingalgorithms known in the art (e.g., algorithms for training neuralnetworks and/or other approaches described herein). After the model istrained, feature values may be generated for IM_(ROI) for which thelabel (physiological response) is unknown, and the computer can utilizethe model to detect the physiological response based on these featurevalues.

It is to be noted that in some embodiments, the model is trained basedon data that includes measurements of the user, in which case it may beconsidered a personalized model of the user. In other embodiments, themodel is trained based on data that includes measurements of one or moreother users, in which case it may be considered a general model.

In order to achieve a robust model, which may be useful for detectingthe physiological response in various conditions, in some embodiments,the samples used in the training may include samples based on IM_(ROI)taken in different conditions and include samples with various labels(e.g., expressing or not expressing certain emotions, or differentvalues of physiological signals). Optionally, the samples are generatedbased on IM_(ROI) taken on different days.

The following are four examples of different compositions of samplesthat may be used when training the model in different embodiments. The“measured user” in the four examples below may be “the user” who ismentioned above (e.g., when the model is a personalized model that wastrained on data that includes measurements of the user), or a user fromamong one or more other users (e.g., when the model is a general modelthat was trained on data that includes measurements of the other users).In a first example, the system does not occlude the ROI, and the modelis trained on samples generated from a first set of IM_(ROI) taken whilethe measured user was indoors and not in direct sunlight, and is alsotrained on other samples generated from a second set of IM_(ROI) takenwhile the measured user was outdoors, in direct sunlight. In a secondexample, the model is trained on samples generated from a first set ofIM_(ROI) taken during daytime, and is also trained on other samplesgenerated from a second set of IM_(ROI) taken during nighttime. In athird example, the model is trained on samples generated from a firstset of IM_(ROI) taken while the measured user was exercising and moving,and is also trained on other samples generated from a second set ofIM_(ROI) taken while the measured user was sitting and not exercising.And a fourth example, the model is trained on samples generated from afirst set of IM_(ROI) taken less than 30 minutes after the measured userhad an alcoholic beverage, and is also trained on other samplesgenerated from a second set of IM_(ROI) taken on a day in which themeasured user did not have an alcoholic beverage.

Labels for the samples may be obtained from various sources. In oneembodiment, the labels may be obtained utilizing one or more sensorsthat are not VCAM_(in). In one example, a heart rate and/or heart ratevariability may be measured using an ECG sensor. In another example, thebreathing rate may be determined using a smart shirt with sensorsattached to the chest (e.g., a smart shirt by Hexoskin®). In yet anotherexample, a type emotional response of the user may be determined basedon analysis of a facial expression made by the user, analysis of theuser's voice, analysis of thermal measurements of regions of the face ofthe user, and/or analysis of one or more of the followingsensor-measured physiological signals of the user: a heart rate, heartrate variability, breathing rate, and galvanic skin response.

In another embodiment, a label describing an emotional response of theuser may be inferred. In one example, the label may be based on semanticanalysis of a communication of the user, which is indicative of theuser's emotional state at the time IM_(ROI) were taken. In anotherexample, the label may be generated in a process in which the user isexposed to certain content, and a label is determined based on anexpected emotional response corresponding to the certain content (e.g.,happiness is an expected response to a nice image while distress is anexpected response to a disturbing image).

Due to the nature of the physiological responses being detected and thetype of data (video images), a machine learning approach that may beapplied in some embodiments is “deep learning”. In one embodiment, themodel may include parameters describing multiple hidden layers of aneural network. Optionally, the model may include a convolution neuralnetwork (CNN). In one example, the CNN may be utilized to identifycertain patterns in the video images, such as the patterns of thereflected FSCC due to the physiological response. Optionally, detectingthe physiological response may be done based on multiple, possiblysuccessive, images that display a certain pattern of change over time(i.e., across multiple frames), which characterizes the physiologicalresponse being detected. Thus, detecting the physiological response mayinvolve retaining state information that is based on previous images.Optionally, the model may include parameters that describe anarchitecture that supports such a capability. In one example, the modelmay include parameters of a recurrent neural network (RNN), which is aconnectionist model that captures the dynamics of sequences of samplesvia cycles in the network's nodes. This enables RNNs to retain a statethat can represent information from an arbitrarily long context window.In one example, the RNN may be implemented using a long short-termmemory (LSTM) architecture. In another example, the RNN may beimplemented using a bidirectional recurrent neural network architecture(BRNN).

Some of the prior art references mentioned herein provide additionaldetailed examples of machine learning-based approaches that may beutilized to detect the physiological response (especially in the case inwhich it corresponds to an emotional response). In one example, Ramirez,et al. (“Color analysis of facial skin: Detection of emotional state”)describe detection of an emotional state using various machine learningalgorithms including decision trees, multinomial logistic regression,and latent-dynamic conditional random fields. In another example, Wang,et al. (“Micro-expression recognition using color spaces”) describevarious feature extraction methods and pixel color valuetransformations, which are used to generate inputs for a support vectormachine (SVM) classifier trained to identify microexpressions.

As mentioned above, in some embodiments, IM_(ENV) may be utilized in thedetection of the physiological response to account, at least in part,for illumination interferences that may lead to errors in the detectionof the physiological response. There are different ways in whichIM_(ENV) may be utilized for this purpose.

In one embodiment, when variations in IM_(ENV) reach a certain threshold(e.g., which may correspond to ambient light variations above a certainextent), the computer may refrain from detecting the physiologicalresponse.

In another embodiment, IM_(ENV) may be utilized to normalize IM_(ROI)with respect to the ambient light. For example, the intensity of pixelsin IM_(ROI) may be adjusted based on the intensity of pixels in IM_(ENV)when IM_(ROI) were taken. US patent application number 20130215244describes a method of normalization in which values of pixels from aregion that does not contain a signal (e.g., background regions thatinclude a different body part of the user or an object behind the user)are subtracted from regions of the image that contain the signal of thephysiological response. While the computational approach describedtherein may be applied to embodiments in this disclosure, the exactsetup described therein may not work well in some cases due to the closeproximity of VCAM_(in) to the face and the fact that VCAM_(in) ishead-mounted. Thus, it may be advantageous to subtract a signal from theenvironment (IM_(ENV)) that is obtained from VCAM_(out), which may moreaccurately represent the ambient light illuminating the face.

It is to be noted that training data that includes a ground-truth signal(i.e., values of the true physiological response corresponding toIM_(ROI) and IM_(ENV)) may be utilized to optimize the normalizationprocedure used to correct IM_(ROI) with respect to the ambient lightmeasured in IM_(ENV). For example, such optimization may be used todetermine parameter values of a function that performs the subtractionabove, which lead to the most accurate detections of the physiologicalresponse.

In still another embodiment, IM_(ENV) may be utilized to generatefeature values in addition to IM_(ROI). Optionally, at least some of thesame types of feature values generated based on IM_(ROI) may also begenerated based on IM_(ENV). Optionally, at least some of the featurevalues generated based on IM_(ENV) may relate to portions of images,such as average intensity of patches of pixels in IM_(ENV).

By utilizing IM_(ENV) as inputs used for the detection of thephysiological response, a machine learning-based model may be trained tobe robust, and less susceptible, to environmental interferences such asambient light variations. For example, if the training data used totrain the model includes samples in which no physiological response waspresent (e.g., no measured emotional response or microexpression wasmade), but some ambient light variations might have introduced someFSCC-related signal, the model will be trained such that feature valuesbased on IM_(ENV) are used to account for such cases. This can enablethe computer to negate, at least in part, the effects of suchenvironmental interferences, and possibly make more accurate detectionsof the physiological response.

In one embodiment, the computer receives an indication indicative of theuser consuming a confounding substance that is expected to affect FSCC(e.g., alcohol, drugs, certain medications, and/or cigarettes). Thecomputer detects the physiological response, while the consumedconfounding substance affects FSCC, based on: IM_(ROI), the indication,and a model that was trained on: a first set of IM_(ROI) taken while theconfounding substance affected FSCC, and a second set of IM_(ROI) takenwhile the confounding substance did not affect FSCC.

Prior art FSCC systems are sensitive to user movements and do notoperate well while the user is running. This is because state-of-the-artFSCC systems use hardware and automatic image trackers that are notaccurate enough to crop correctly the ROI from the entire image whilerunning, and the large errors in cropping the ROI are detrimental to theperformances of the FSCC algorithms. Contrary to the prior art FSCCsystems, the disclosed VCAM_(in) remains pointed at its ROI also whenthe user's head makes angular and lateral movements, and thus thecomplicated challenges related to image registration and ROI trackingare much simplified or even eliminated Therefore, systems based onVCAM_(in) (such as the one illustrated in FIG. 47) may detect thephysiological response (based on FSCC) also while the user is running.

VCAM_(in) may be pointed at different regions on the face. In a firstembodiment, the ROI is on the forehead, VCAM_(in) is located less than10 cm from the user's face, and optionally the optical axis of VCAM_(in)is above 20° from the Frankfort horizontal plane. In a secondembodiment, the ROI is on the nose, and VCAM_(in) is located less than10 cm from the user's face. Because VCAM_(in) is located close to theface, it is possible to calculate the FSCC based on a small ROI, whichis irrelevant to the non-head-mounted prior arts that are limited by theaccuracy of their automatic image tracker. In a third embodiment,VCAM_(in) is pointed at an eye of the user. The computer selects thesclera as the ROI and detects the physiological response based on colorchanges recognizable in IM_(ROI) of the sclera. In a fourth embodiment,VCAM_(in) is pointed at an eye of the user. The computer selects theiris as the ROI and detects the physiological response based on colorchanges recognizable in IM_(ROI) of the iris. Optionally, the computerfurther calculates changes to the pupil diameter based on the IM_(ROI)of the iris, and detects an emotional response of the user based on thechanges to the pupil diameter.

In order to improve the detection accuracy, and in some cases in orderto better account for interferences, the computer may utilizemeasurements of one or more head-mounted thermal cameras in thedetection of the physiological response. In one embodiment, the systemmay include an inward-facing head-mounted thermal camera that takesthermal measurements of a second ROI (TH_(ROI2)) on the user's face.Optionally, ROI and ROI₂ overlap, and the computer utilizes TH_(ROI2) todetect the physiological response. Optionally, on average, detecting thephysiological response based on both FSCC recognizable in IM_(ROI) andTH_(ROI2) is more accurate than detecting the physiological responsebased on the FSCC without TH_(ROI2). Optionally, the computer utilizesTH_(ROI2) to account, at least in part, for temperature changes, whichmay occur due to physical activity and/or consumption of certainmedications that affect the blood flow. Optionally, the computerutilizes TH_(ROI2) by generating feature values based on TH_(ROI2), andutilizing a model that was trained on data comprising TH_(ROI2) in orderto detect the physiological response.

In another embodiment, the system may include an outward-facinghead-mounted thermal camera that takes thermal measurements of theenvironment (TH_(ENV)). Optionally, the computer may utilize TH_(ENV) todetect the physiological response (e.g., by generating feature valuesbased on TH_(ENV) and utilizing a model trained on data comprisingTH_(ENV)). Optionally, on average, detecting the physiological responsebased on both FSCC recognizable in IM_(ROI) and TH_(ENV) is moreaccurate than detecting the physiological response based on the FSCCwithout TH_(ENV). Optionally, the computer utilizes TH_(ENV) to account,at least in part, for thermal interferences from the environment, suchas direct sunlight and/or a nearby heater.

In addition to detecting a physiological response, in some embodiments,the computer may utilize IM_(ROI) to generate an avatar of the user(e.g., in order to represent the user in a virtual environment).Optionally, the avatar may express emotional responses of the user,which are detected based on IM_(ROI). Optionally, the computer maymodify the avatar of the user to show synthesized facial expressionsthat are not manifested in the user's actual facial expressions. In oneembodiment, the synthesized facial expressions correspond to emotionalresponses detected based on FSCC that are recognizable in IM_(ROI). Inanother embodiment, the synthesized facial expressions correspond toemotional responses detected based on thermal measurements taken by CAM.Some of the various approaches that may be utilized to generate theavatar based on IM_(ROI) are described in co-pending US patentpublication 2016/0360970.

The following method for detecting a physiological response based onfacial skin color changes (FSCC) may be used by systems modeledaccording to FIG. 47. The steps described below may be performed byrunning a computer program having instructions for implementing themethod. Optionally, the instructions may be stored on acomputer-readable medium, which may optionally be a non-transitorycomputer-readable medium. In response to execution by a system includinga processor and memory, the instructions cause the system to perform thefollowing steps:

In Step 1, taking images of a region of interest (IM_(ROI)) on a user'sface utilizing an inward-facing head-mounted visible-light camera(VCAM_(in)). The ROI is illuminated by ambient light.

And in Step 2, detecting the physiological response based on FSCCrecognizable in IM_(ROI). Optionally, detecting the physiologicalresponse involves generating feature values based on IM_(ROI) andutilizing a model to calculate, based on the feature values, a valueindicative of an extent of the physiological response. Optionally, themodel was trained based on IM_(ROI) of the user taken during differentdays.

In one embodiment, the method may optionally include a step of takingimages of the environment (IM_(ENV)) utilizing an outward-facinghead-mounted visible-light camera (VCAM_(out)). Optionally, detectingthe physiological response is also based on IM_(ENV).

Normally, the lens plane and the sensor plane of a camera are parallel,and the plane of focus (PoF) is parallel to the lens and sensor planes.If a planar object is also parallel to the sensor plane, it can coincidewith the PoF, and the entire object can be captured sharply. If the lensplane is tilted (not parallel) relative to the sensor plane, it will bein focus along a line where it intersects the PoF. The Scheimpflugprinciple is a known geometric rule that describes the orientation ofthe plane of focus of a camera when the lens plane is tilted relative tothe sensor plane.

FIG. 22a is a schematic illustration of an inward-facing head-mountedcamera 550 embedded in an eyeglasses frame 551, which utilizes theScheimpflug principle to improve the sharpness of the image taken by thecamera 550. The camera 550 includes a sensor 558 and a lens 555. Thetilt of the lens 555 relative to sensor 558, which may also beconsidered as the angle between the lens plane 555 and the sensor plane559, is determined according to the expected position of the camera 550relative to the ROI 552 when the user wears the eyeglasses. For arefractive optical lens, the “lens plane” 556 refers to a plane that isperpendicular to the optical axis of the lens 555. Herein, the singularalso includes the plural, and the term “lens” refers to one or morelenses. When “lens” refers to multiple lenses (which is usually the casein most modern cameras having a lens module with multiple lenses), thenthe “lens plane” refers to a plane that is perpendicular to the opticalaxis of the lens module.

The Scheimpflug principle may be used for both thermal cameras (based onlenses and sensors for wavelengths longer than 2500 nm) andvisible-light and/or near-IR cameras (based on lenses and sensors forwavelengths between 400-900 nm). FIG. 22b is a schematic illustration ofa camera that is able to change the relative tilt between its lens andsensor planes according to the Scheimpflug principle. Housing 311 mountsa sensor 312 and lens 313. The lens 313 is tilted relative to the sensor312. The tilt may be fixed according to the expected position of thecamera relative to the ROI when the user wears the HMS, or may beadjusted using motor 314. The motor 314 may move the lens 313 and/or thesensor 312.

In one embodiment, an HMS device includes a frame configured to be wornon a user's head, and an inward-facing camera physically coupled to theframe. The inward-facing camera may assume one of two configurations:(i) the inward-facing camera is oriented such that the optical axis ofthe camera is above the Frankfort horizontal plane and pointed upward tocapture an image of a region of interest (ROI) above the user's eyes, or(ii) the inward-facing camera is oriented such that the optical axis isbelow the Frankfort horizontal plane and pointed downward to capture animage of an ROI below the user's eyes. The inward-facing camera includesa sensor and a lens. The sensor plane is tilted by more than 2° relativeto the lens plane according to the Scheimpflug principle in order tocapture a sharper image.

In another embodiment, an HMS includes an inward-facing head-mountedcamera that captures an image of an ROI on a user's face, when worn onthe user's head. The ROI is on the user's forehead, nose, upper lip,cheek, and/or lips. The camera includes a sensor and a lens. And thesensor plane is tilted by more than 2° relative to the lens planeaccording to the Scheimpflug principle in order to capture a sharperimage.

Because the face is not planar and the inward-facing head-mounted camerais located close to the face, an image captured by a camera having awide field of view (FOV) and a low f-number may not be perfectly sharp,even after applying the Scheimpflug principle. Therefore, in someembodiments, the tilt between the lens plane and the sensor plane isselected such as to adjust the sharpness of the various areas covered inthe ROI according to their importance for detecting the user'sphysiological response (which may be the user's emotional response insome cases). In one embodiment, the ROI covers first and second areas,where the first area includes finer details and/or is more important fordetecting the physiological response than the second area. Therefore,the tilt between the lens and sensor planes is adjusted such that theimage of the first area is shaper than the image of the second area.

In another embodiment, the ROI covers both a first area on the upper lipand a second area on a cheek, and the tilt is adjusted such that theimage of the first area is shaper than the image of the second area,possibly because the upper lip usually provides more information and hasmore details relative to the cheek.

In still another embodiment, the ROI covers both a first area on theupper lip and a second area on the nose, and the tilt is adjusted suchthat the image of the first area is shaper than the image of the secondarea, possibly because the upper lip usually provides more informationrelative to the nose.

In still another embodiment, the ROI covers a first area on the cheekstraight above the upper lip, a second area on the cheek from the edgeof the upper lip towards the ear, and a third area on the nose. And thetilt between the lens plane and the sensor plane is adjusted such thatthe image of the first area is shaper than both the images of the secondand third areas.

In still another embodiment, the ROI covers both a first area on thelips and a second area on the chin, and the tilt is adjusted such thatthe image of the first area is shaper than the image of the second area,possibly because the lips usually provides more information than thechin.

In still another embodiment, the camera is a visible-light camera, andthe ROI covers both a first area on the lower forehead (including aneyebrow) and a second area on the upper forehead, and the tilt isadjusted such that the image of the first area is shaper than the imageof the second area, possibly because the eyebrow provides moreinformation about the user's emotional response than the upper forehead.

In still another embodiment, the camera is a thermal camera, and the ROIcovers an area on the forehead, and the tilt is adjusted such that theimage of a portion of the middle and upper part of the forehead (belowthe hair line) is shaper than the image of a portion of the lower partof the forehead, possibly because the middle and upper parts of theforehead are more indicative of prefrontal cortex activity than thelower part of the forehead, and movements of the eyebrows disturb thethermal measurements of the lower part of the forehead.

In one embodiment, the tilt between the lens plane and sensor plane isfixed. The fixed tilt is selected according to an expected orientationbetween the camera and the ROI when a user wears the frame. Having afixed tilt between the lens and sensor planes may eliminate the need foran adjustable electromechanical tilting mechanism. As a result, a fixedtilt may reduce the weight and cost of the camera, while still providinga sharper image than an image that would be obtained from a similarcamera in which the lens and sensor planes are parallel. The magnitudeof the fixed tilt may be selected according to facial dimensions of anaverage user expected to wear the system, or according to a model of thespecific user expected to wear the system in order to obtain thesharpest image.

In another embodiment, the system includes an adjustableelectromechanical tilting mechanism configured to change the tiltbetween the lens and sensor planes according to the Scheimpflugprinciple based on the orientation between the camera and the ROI whenthe frame is worn by the user. The tilt may be achieved using at leastone motor, such as a brushless DC motor, a stepper motor (without afeedback sensor), a brushed DC electric motor, a piezoelectric motor,and/or a micro-motion motor.

The adjustable electromechanical tilting mechanism configured to changethe tilt between the lens and sensor planes may include one or more ofthe following mechanisms: (i) a mirror that changes its angle; (ii) adevice that changes the angle of the lens relative to the sensor; and/or(iii) a device that changes the angle of the sensor relative to thelens. In one embodiment, the camera, including the adjustableelectromechanical tilting mechanism, weighs less than 10 g, and theadjustable electromechanical tilting mechanism is able to change thetilt in a limited range below 30° between the two utmost orientationsbetween the lens and sensor planes. Optionally, the adjustableelectromechanical tilting mechanism is able to change the tilt in alimited range below 20° between the two utmost orientations between thelens and sensor planes. In another embodiment, the adjustableelectromechanical tilting mechanism is able to change the tilt in alimited range below 10°. In some embodiments, being able to change thetilt in a limited range reduces at least one of the weight, cost, andsize of the camera, which is advantageous for a wearable device. In oneexample, the camera is manufactured with a fixed predetermined tiltbetween the lens and sensor planes, which is in addition to the tiltprovided by the adjustable electromechanical tilting mechanism. Thefixed predetermined orientation may be determined according to theexpected orientation between the camera and the ROI for an average user,such that the adjustable electromechanical tilting mechanism is used tofine-tune the tilt between the lens and sensor planes for the specificuser who wears the frame and has facial dimensions that are differentfrom the average user.

Various types of cameras may be utilized in different embodimentsdescribed herein. In one embodiment, the camera is a thermal camera thattakes thermal measurements of the ROI with a focal plane array thermalsensor having an angle above 2° between the lens and sensor planes.Optionally, the thermal camera weighs below 10 g, is located less than10 cm from the user's face, and the tilt of the lens plane relative tothe sensor plane is fixed. The fixed tilt is selected according to anexpected orientation between the camera and the ROI when the user wearsthe frame. Optionally, the system includes a computer to detect aphysiological response based on the thermal measurements. Optionally,the computer processes time series measurements of each sensing elementindividually to detect the physiological response.

In another embodiment, the camera is a visible-light camera that takesvisible-light images of the ROI, and a computer generates an avatar forthe user based on the visible-light images. Some of the variousapproaches that may be utilized to generate the avatar based on thevisible-light images are described in co-pending US patent publication2016/0360970. Additionally or alternatively, the computer may detect anemotional response of the user based on (i) facial expressions in thevisible-light images utilizing image processing, and/or (ii) facial skincolor changes (FSCC), which result from concentration changes ofhemoglobin and/or oxygenation.

It is to be noted that there are various approaches known in the art foridentifying facial expressions from images. While many of theseapproaches were originally designed for full-face frontal images, thoseskilled in the art will recognize that algorithms designed for full-facefrontal images may be easily adapted to be used with images obtainedusing the inward-facing head-mounted visible-light cameras disclosedherein. For example, the various machine learning techniques describedin prior art references may be applied to feature values extracted fromimages that include portions of the face from orientations that are notdirectly in front of the user. Furthermore, due to the closeness of thevisible-light cameras to the face, facial features are typically largerin images obtained by the systems described herein. Moreover, challengessuch as image registration and face tracking are vastly simplified andpossibly non-existent when using inward-facing head-mounted cameras. Thereference Zeng, Zhihong, et al. “A survey of affect recognition methods:Audio, visual, and spontaneous expressions.” IEEE transactions onpattern analysis and machine intelligence 31.1 (2009): 39-58, describessome of the algorithmic approaches that may be used for this task. Thefollowing references discuss detection of emotional responses based onFSCC: (i) Ramirez, Geovany A., et al. “Color analysis of facial skin:Detection of emotional state” in Proceedings of the IEEE Conference onComputer Vision and Pattern Recognition Workshops, 2014; and (ii) Wang,Su-Jing, et al. “Micro-expression recognition using color spaces”, inIEEE Transactions on Image Processing 24.12 (2015): 6034-6047.

In still another embodiment, the camera is a light field camera thatimplements a predetermined blurring at a certain Scheimpflug angle, anddecodes the predetermined blurring as function of the certainScheimpflug angle. The light field camera may include an autofocusing ofthe image obtained using the tilting mechanism based on the principlethat scene points that are not in focus are blurred while scene pointsin focus are sharp. The autofocusing may study a small region around agiven pixel; the region is expected to get sharper as the Scheimpflugadjustment gets better, and vice versa. Additionally or alternatively,the autofocusing may use the variance of the neighborhood around eachpixel as a measure of sharpness, where a proper Scheimpflug adjustmentshould increase the variance.

Thermal and/or FSCC patterns corresponding to physiological responsesmay show high variability between different users due to variability ofthe their brains, blood vessel locations, skin properties, hair,physical conditions, and face shapes and sizes. Thus, patterns and/orvarious extractable features from one user's thermal and/or FSCC datamay not be easily transferable to another user, or even to the same userunder different physiological and/or mental conditions. Therefore, someof the embodiments described herein involve training personalized modelsinvolving thermal and/or FSCC patterns that are predictive of varioususer-defined categories of experiencing and/or perceiving certainevents. Personalized models can overcome some of the possibledisadvantages of using normed physiological statistics, which paves theway for personalized training, detection, and therapies, which are ableto account for arbitrary user-defined physiological and/or mental statescorresponding to a wide variety of individual needs. Leveraging machinelearning algorithms can enable assignment of arbitrary user-definedphysiological and/or mental states to recorded thermal and/or FSCC dataduring day-to-day activities, which are later used as basis forautomatic detection and/or therapies for the user, optionally withoutinvolving a clinician.

The personalized model does not need to correspond to a standarduniversally applicable pattern, and thus the user may be free to definehis/her arbitrary user-defined physiological and/or mental states. Inother words, in addition to (or instead of) detecting a state thatcorresponds to some arbitrary population average, the personalized modelallows a personalized detection of a user-defined state.

One embodiment in which a personalized model is utilized involves atraining phase and an operation phase. In the training phase, the systemidentifies desired and/or undesired physiological and/or mental statesof the user using active methods (e.g., the user presses a button)and/or passive methods (e.g., applying semantic analysis to the user'sspeech and typing). The system may also continue to update thepersonalized model to accommodate for changes over time, to supportsincreased efficacy, and to identify new personalized states beyond thoserepresented by population average. Instead of relying on a model trainedbased on data obtained from a wide population, the personalized modelmay decouple commonly coupled ROIs and/or putative physiologicalresponses from the applications, allowing the user to train the systemto detect arbitrary personalized thermal and/or FSCC patterns that maynot suite the wide population. Training the personalized model may bebased on known machine learning methods such as neural networks,supervised machine learning, pattern recognition, pattern matching, etc.The system may detect, predict, and train for the arbitrary user-definedphysiological and/or mental states, identified by personalized thermaland/or FSCC patterns, not limited to averages obtained from a widepopulation.

In the operation phase, the system alerts, predicts, and/or treats theuser based on the personalized model. The system may alert when the useris in the desired/undesired state, predict when the user is going to bein that state, and treat the user to get into a desired state or avoidan undesired state by providing a feedback. The operation phase mayinclude known biofeedback/neurofeedback interactive sessions tuned toguide the user towards the user-defined personalized physiologicaland/or mental states. For example, the personalized model may be trainedto guide the user towards flow, creativity, curiosity, compassion,and/or happiness states, as defined and experienced by the user, and toalert against anger, aggression, boredom, and/or sadness, also asdefined and experienced by the user, without these necessarily beingsuitable for the wide population.

One embodiment of a method for personalized thermal and/or FSCCdetection includes a timestamping step, a machine learning step, arefinement step, an optional detection step, and an optional biofeedbackstep (where biofeedback refers also to neurofeedback).

In the timestamping step, an HMS records arbitrary user-definedphysiological and/or mental states for personal use. The user mayprovide, via a user interface, timestamped markers on the recorded dataused as labels by machine learning approaches for detecting targetuser-defined physiological and/or mental states (which may be desired orundesired states). When the user engages in a certain task, and as theuser enters a target state, the user may (via a user interface) manualprovide a timestamp to mark the time of entering into the target state,and/or the computer may set an automated timestamp based on inferringthe entering into the target state from the user's performance and/oractivities (for example, using predetermined limits of performance thatonce reached automatically trigger timestamping the recorded data asentering into the target state). Upon leaving the target state, the usermay provide a timestamp to mark the leaving of the target state, and/orthe computer may set an automated timestamp based on inferring theleaving of the target state from the user's performance and/oractivities. Several iterations involving timestamping of entering andleaving the target state may complete the timestamping step.

In the machine learning step, the computer extracts and selects featuresfrom the thermal and/or FSCC measurements, labels the extracted andselected features according to the timestamps, and tries one or moremachine learning algorithms to train a classifier, while treating themeasurements as the training and testing sets. Optionally, for uniquepersonalized states, the machine learning algorithm may be optimized forcross-validation by splitting the training set into a first part usedfor training and a second part used for testing. In addition, testingsets comprising data of other users may be used to measure theclassifier's generality. The following examples illustrate various waysto label the HMS measurements based on the timestamps.

In a first example, the computer may (i) label as “not desired” TH_(ROI)taken before receiving from the user a first timestamp marking theentering into a desired state, (ii) label as “desired” TH_(ROI) takenafter receiving the first timestamp and before receiving a secondtimestamp marking the leaving of the desired state, and (iii) label as“not desired” TH_(ROI) taken after receiving the second timestamp.Optionally, the computer may label as “unknown” TH_(ROI) takensufficiently before receiving the first timestamp and TH_(ROI) takensufficiently after receiving the second timestamp.

In a second example, the computer may (i) label as “leading to headache”TH_(ROI) taken during a first window of time before receiving from theuser a first timestamp marking occurrence of a headache, (ii) label as“headache” TH_(ROI) taken after receiving the first timestamp and untila second window before receiving from the user a second timestampmarking “no headache”, (iii) label as “headache leaving” TH_(ROI) takenduring the second window, and (iv) label as “no headache” TH_(ROI) takenafter receiving the second timestamp.

In a third example, the computer may (i) label as “leading to asthmaattack” TH_(breath) indicative of the user's breathing pattern (such asthermal measurements of a region on the upper lip) taken during a firstwindow before identifying that the user uses a first inhaler, (ii) labelas “first inhaler immediate effect” TH_(breath) taken during a secondwindow after using the first inhaler, (iii) label as “first inhaler longeffect” TH_(breath) taken during a third window following the secondwindow, and (iv) label as “second inhaler immediate effect” TH_(breath)taken during a fourth window after identifying that the user uses asecond inhaler Optionally, the computer may use the automated labelingfor assessing the user's reaction to using the first inhaler vs usingthe second inhaler.

In a fourth example, the computer may (i) label as “buildingconcentration” TH_(breath) indicative of the user's breathing patternand TH_(forehead) indicative of a thermal pattern on the user's foreheadtaken while the user's software agent indicates that the user does notcheck distracting websites (such as social networks, news and email) butthe user's gaze is not essentially continuously focused on the screen,(ii) label as “concentrated” TH_(breath) and TH_(forehead) taken whilethe software agent indicates that the user's gaze is continuouslyfocused on the screen and until a certain duration before the user lostconcentration, and (iii) label as “start losing concentration”TH_(breath) and TH_(forehead) taken during the certain duration.

In a fifth example, the computer may (i) label as “possibly happy”TH_(ROI) and FSCC taken during a first window before a speech analysismodule provides a timestamp that the user is happy, (ii) label as“happy” TH_(ROI) and FSCC taken during a second window after receivingthe timestamp, and (iii) label as “angry” TH_(ROI) and FSCC taken duringa third window after the speech analysis module provides a timestampthat the user is angry.

In the refinement step, the computer starts guessing the physiologicaland/or mental states, and asks the user to confirm correct, incorrect,or inapplicable status of the guesses. The refinement step increasesfidelity the more it is performed.

In the optional detection step, the computer analyzes in real timefeature values generated based on the thermal and/or FSCC measurementsin order to alert the user about entering and/or leaving a target state.For example, the computer permits administration of pain medication tothe user after the classifier determines that the user experiences painabove a threshold previously determined by the user during thetimestamping step. This may reduce addiction by reducing unnecessaryadministrations of higher dose pain medication. Additionally, the usermay be trained to control his/her pain perception during the biofeedbackstep, which may be more effective after a personalized model has beenapplied.

In the optional biofeedback step, the computer generates a feedback forthe user based on the personalized target state. The biofeedback stepmay use a standard biofeedback protocol, but instead of training theuser towards achieving externally derived thermal and/or FSCC targetpatterns that suit the wide population, the user is trained to achievepersonalized thermal and/or FSCC target patterns that most closelyresemble the thermal and/or FSCC patterns found to be predictive duringthe timestamping and refinement steps.

In one embodiment, the user labels during the timestamping step pairs ofundesired and desired states (such as pain vs no pain, migraine vs nomigraine, angry vs calmed, stressed vs calmed, concentrated vs notconcentrated, sad vs happy, self-focused vs compassionate). Then thebiofeedback step trains the user to move out of the undesired state by(i) encouraging changes that bring the current measured thermal and/orFSCC pattern closer to the desired personalized thermal and/or FSCCpattern found to be predictive during the timestamping and refinementsteps, and (ii) discouraging changes that bring the current measuredthermal and/or FSCC pattern closer to the undesired personalized thermaland/or FSCC pattern found to be predictive during the timestamping andrefinement steps.

The following is one example of the information flow in an HMS thatincludes a head-mounted thermal camera and a computer. In thetimestamping step, the head-mounted thermal camera takes thermalmeasurements, and the user (or computer) adds manual (or automated)timestamps for entering and/or leaving a target state. The timestampingstep feeds the machine learning step, in which a machine learning-basedtraining algorithm is used to train a personalized model that isevaluated against user measurements in known states. The machinelearning step feeds the refinement step with processed data andquestions, and in the refinement step the user answers whether themachine learning algorithm has correctly detected the user's state. Boththe machine learning step and the refinement step may provide data tothe optional detection and biofeedback steps (which may communicate witheach other).

Big data analysis may be performed to identify trends and detect newcorrelations over users and populations, together with other sources ofinformation, such as other wearable devices (e.g., smart watches, smartshirts, EEG headsets, smart earphones), mobile devices (e.g.,smartphone, laptop), and other sources of information (e.g., socialnetworks, search engines, bots, software agents, medical records, IoTdevices).

Various embodiments described herein involve an HMS that may beconnected, using wires and/or wirelessly, with a device carried by theuser and/or a non-wearable device. The HMS may include a battery, acomputer, sensors, and a transceiver.

FIG. 57a and FIG. 57b are schematic illustrations of possibleembodiments for computers (400, 410) that are able to realize one ormore of the embodiments discussed herein that include a “computer”. Thecomputer (400, 410) may be implemented in various ways, such as, but notlimited to, a microcontroller, a computer on a chip, a system-on-chip(SoC), a system-on-module (SoM), a processor with its requiredperipherals, a server computer, a client computer, a personal computer,a cloud computer, a network device, a handheld device (e.g., asmartphone), an head-mounted system (such as smartglasses, an augmentedreality system, a virtual reality system, and/or a smart-helmet), acomputing device embedded in a wearable device (e.g., a smartwatch or acomputer embedded in clothing), a computing device implanted in thehuman body, and/or any other computer form capable of executing a set ofcomputer instructions. Further, references to a computer or a processorinclude any collection of one or more computers and/or processors (whichmay be at different locations) that individually or jointly execute oneor more sets of computer instructions. This means that the singular term“computer” is intended to imply one or more computers, which jointlyperform the functions attributed to “the computer”. In particular, somefunctions attributed to the computer may be performed by a computer on awearable device (e.g., smartglasses) and/or a computer of the user(e.g., smartphone), while other functions may be performed on a remotecomputer, such as a cloud-based server.

The computer 400 includes one or more of the following components:processor 401, memory 402, computer readable medium 403, user interface404, communication interface 405, and bus 406. The computer 410 includesone or more of the following components: processor 411, memory 412, andcommunication interface 413.

Thermal measurements that are forwarded to a processor/computer mayinclude “raw” values that are essentially the same as the valuesmeasured by thermal cameras, and/or processed values that are the resultof applying some form of preprocessing and/or analysis to the rawvalues. Examples of methods that may be used to process the raw valuesinclude analog signal processing, digital signal processing, and variousforms of normalization, noise cancellation, and/or feature extraction.

Functionality of various embodiments may be implemented in hardware,software, firmware, or any combination thereof. If implemented at leastin part in software, implementing the functionality may involve acomputer program that includes one or more instructions or code storedor transmitted on a computer-readable medium and executed by one or moreprocessors. Computer-readable media may include computer-readablestorage media, which corresponds to a tangible medium such as datastorage media, or communication media including any medium thatfacilitates transfer of a computer program from one place to another.Computer-readable medium may be any media that can be accessed by one ormore computers to retrieve instructions, code, data, and/or datastructures for implementation of the described embodiments. A computerprogram product may include a computer-readable medium. In one example,the computer-readable medium 403 may include one or more of thefollowing: RAM, ROM, EEPROM, optical storage, magnetic storage, biologicstorage, flash memory, or any other medium that can store computerreadable data.

A computer program (also known as a program, software, softwareapplication, script, program code, or code) can be written in any formof programming language, including compiled or interpreted languages,declarative or procedural languages. The program can be deployed in anyform, including as a standalone program or as a module, component,subroutine, object, or another unit suitable for use in a computingenvironment. A computer program may correspond to a file in a filesystem, may be stored in a portion of a file that holds other programsor data, and/or may be stored in one or more files that may be dedicatedto the program. A computer program may be deployed to be executed on oneor more computers that are located at one or more sites that may beinterconnected by a communication network.

Computer-readable medium may include a single medium and/or multiplemedia (e.g., a centralized or distributed database, and/or associatedcaches and servers) that store one or more sets of instructions. Invarious embodiments, a computer program, and/or portions of a computerprogram, may be stored on a non-transitory computer-readable medium, andmay be updated and/or downloaded via a communication network, such asthe Internet. Optionally, the computer program may be downloaded from acentral repository, such as Apple App Store and/or Google Play.Optionally, the computer program may be downloaded from a repository,such as an open source and/or community run repository (e.g., GitHub).

At least some of the methods described herein are “computer-implementedmethods” that are implemented on a computer, such as the computer (400,410), by executing instructions on the processor (401, 411).Additionally, at least some of these instructions may be stored on anon-transitory computer-readable medium.

Herein, a direction of the optical axis of a VCAM or a CAM that hasfocusing optics is determined by the focusing optics, while thedirection of the optical axis of a CAM without focusing optics (such asa single pixel thermopile) is determined by the angle of maximumresponsivity of its sensor. When optics are utilized to takemeasurements with a CAM, then the term CAM includes the optics (e.g.,one or more lenses). In some embodiments, the optics of a CAM mayinclude one or more lenses made of a material suitable for the requiredwavelength, such as one or more of the following materials: CalciumFluoride, Gallium Arsenide, Germanium, Potassium Bromide, Sapphire,Silicon, Sodium Chloride, and Zinc Sulfide. In other embodiments, theCAM optics may include one or more diffractive optical elements, and/oror a combination of one or more diffractive optical elements and one ormore refractive optical elements.

When CAM includes an optical limiter/field limiter/FOV limiter (such asa thermopile sensor inside a standard TO-39 package with a window, or athermopile sensor with a polished metal field limiter), then the termCAM may also refer to the optical limiter. Depending on the context, theterm CAM may also refer to a readout circuit adjacent to CAM, and/or tothe housing that holds CAM.

Herein, references to thermal measurements in the context of calculatingvalues based on thermal measurements, generating feature values based onthermal measurements, or comparison of thermal measurements, relate tothe values of the thermal measurements (which are values of temperatureor values of temperature changes). Thus, a sentence in the form of“calculating based on TH_(ROI)” may be interpreted as “calculating basedon the values of TH_(ROI)”, and a sentence in the form of “comparingTH_(ROI1) and TH_(ROI2)” may be interpreted as “comparing values ofTH_(ROI1) and values of TH_(ROI2)”.

Depending on the embodiment, thermal measurements of an ROI (usuallydenoted TH_(ROI) or using a similar notation) may have various forms,such as time series, measurements taken according to a varying samplingfrequency, and/or measurements taken at irregular intervals. In someembodiments, thermal measurements may include various statistics of thetemperature measurements (T) and/or the changes to temperaturemeasurements (ΔT), such as minimum, maximum, and/or average values.Thermal measurements may be raw and/or processed values. When a thermalcamera has multiple sensing elements (pixels), the thermal measurementsmay include values corresponding to each of the pixels, and/or includevalues representing processing of the values of the pixels. The thermalmeasurements may be normalized, such as normalized with respect to abaseline (which is based on earlier thermal measurements), time of day,day in the month, type of activity being conducted by the user, and/orvarious environmental parameters (e.g., the environment's temperature,humidity, radiation level, etc.).

As used herein, references to “one embodiment” (and its variations) meanthat the feature being referred to may be included in at least oneembodiment of the invention. Moreover, separate references to “oneembodiment”, “some embodiments”, “another embodiment”, “still anotherembodiment”, etc., may refer to the same embodiment, may illustratedifferent aspects of an embodiment, and/or may refer to differentembodiments.

Some embodiments may be described using the verb “indicating”, theadjective “indicative”, and/or using variations thereof. Herein,sentences in the form of “X is indicative of Y” mean that X includesinformation correlated with Y, up to the case where X equals Y. Forexample, sentences in the form of “thermal measurements indicative of aphysiological response” mean that the thermal measurements includeinformation from which it is possible to infer the physiologicalresponse. Stating that “X indicates Y” or “X indicating Y” may beinterpreted as “X being indicative of Y”. Additionally, sentences in theform of “provide/receive an indication indicating whether X happened”may refer herein to any indication method, including but not limited to:sending/receiving a signal when X happened and not sending/receiving asignal when X did not happen, not sending/receiving a signal when Xhappened and sending/receiving a signal when X did not happen, and/orsending/receiving a first signal when X happened and sending/receiving asecond signal X did not happen.

Herein, “most” of something is defined as above 51% of the something(including 100% of the something). Both a “portion” of something and a“region” of something refer herein to a value between a fraction of thesomething and 100% of the something. For example, sentences in the formof a “portion of an area” may cover between 0.1% and 100% of the area.As another example, sentences in the form of a “region on the user'sforehead” may cover between the smallest area captured by a single pixel(such as 0.1% or 5% of the forehead) and 100% of the forehead. The word“region” refers to an open-ended claim language, and a camera said tocapture a specific region on the face may capture just a small part ofthe specific region, the entire specific region, and/or a portion of thespecific region together with additional region(s).

Sentences in the form of “angle greater than 20°” refer to absolutevalues (which may be +20° or −20° in this example), unless specificallyindicated, such as in a phrase having the form of “the optical axis ofCAM is 20° above/below the Frankfort horizontal plane” where it isclearly indicated that the CAM is pointed upwards/downwards. TheFrankfort horizontal plane is created by two lines from the superioraspects of the right/left external auditory canal to the most inferiorpoint of the right/left orbital rims.

The terms “comprises,” “comprising,” “includes,” “including,” “has,”“having”, or any other variation thereof, indicate an open-ended claimlanguage that does not exclude additional limitations. The “a” or “an”is employed to describe one or more, and the singular also includes theplural unless it is obvious that it is meant otherwise; for example,sentences in the form of “a CAM configured to take thermal measurementsof a region (TH_(ROI))” refers to one or more CAMs that take thermalmeasurements of one or more regions, including one CAM that takesthermal measurements of multiple regions; as another example, “acomputer” refers to one or more computers, such as a combination of awearable computer that operates together with a cloud computer.

The phrase “based on” is intended to mean “based, at least in part, on”.Additionally, stating that a value is calculated “based on X” andfollowing that, in a certain embodiment, that the value is calculated“also based on Y”, means that in the certain embodiment, the value iscalculated based on X and Y.

The terms “first”, “second” and so forth are to be interpreted merely asordinal designations, and shall not be limited in themselves. Apredetermined value is a fixed value and/or a value determined any timebefore performing a calculation that compares a certain value with thepredetermined value. A value is also considered to be a predeterminedvalue when the logic, used to determine whether a threshold thatutilizes the value is reached, is known before start performingcomputations to determine whether the threshold is reached.

The embodiments of the invention may include any variety of combinationsand/or integrations of the features of the embodiments described herein.Although some embodiments may depict serial operations, the embodimentsmay perform certain operations in parallel and/or in different ordersfrom those depicted. Moreover, the use of repeated reference numeralsand/or letters in the text and/or drawings is for the purpose ofsimplicity and clarity and does not in itself dictate a relationshipbetween the various embodiments and/or configurations discussed. Theembodiments are not limited in their applications to the order of stepsof the methods, or to details of implementation of the devices, set inthe description, drawings, or examples. Moreover, individual blocksillustrated in the figures may be functional in nature and therefore maynot necessarily correspond to discrete hardware elements.

Certain features of the embodiments, which may have been, for clarity,described in the context of separate embodiments, may also be providedin various combinations in a single embodiment. Conversely, variousfeatures of the embodiments, which may have been, for brevity, describedin the context of a single embodiment, may also be provided separatelyor in any suitable sub-combination. Embodiments described in conjunctionwith specific examples are presented by way of example, and notlimitation. Moreover, it is evident that many alternatives,modifications, and variations will be apparent to those skilled in theart. It is to be understood that other embodiments may be utilized andstructural changes may be made without departing from the scope of theembodiments. Accordingly, this disclosure is intended to embrace allsuch alternatives, modifications, and variations that fall within thespirit and scope of the appended claims and their equivalents.

We claim:
 1. A system configured to detect fever, comprising: first andsecond inward-facing head-mounted cameras (Cam_(1&2)), located less than5 cm from a user's face, sensitive to wavelengths below 1050 nanometer,and configured to capture images of respective first and second regionson the user's face; wherein middles of the first and second regions areat least 4 cm apart; and a computer configured to: calculate, based onbaseline images captured with Cam_(1&2) while the user did not have afever, a baseline pattern comprising values indicative of first andsecond baseline hemoglobin concentrations at the first and secondregions, respectively; calculate, based on a current set of imagescaptured with Cam_(1&2), a current pattern comprising values indicativeof first and second current hemoglobin concentrations at the first andsecond regions, respectively; and detect whether the user has a feverbased on a deviation of the current pattern from the baseline pattern.2. The system of claim 1, wherein the computer is further configured to:calculate, based on additional baseline images captured with Cam_(1&2)while the user had a fever, a fever-baseline pattern comprising valuesindicative of first and second fever hemoglobin concentrations at thefirst and second regions, respectively; and base the detection ofwhether the user has the fever also on a deviation of the currentpattern from the fever-baseline pattern.
 3. The system of claim 1,wherein the first region is located above the user's eyes, and thesecond region is located below the user's eyes.
 4. The system of claim1, wherein the middle of the first region is located less than 4 cm fromthe vertical symmetric axis of the user's face, and the middle of thesecond region is located more than 4 cm from the vertical symmetricaxis.
 5. The system of claim 1, wherein the baseline images and thecurrent set of images comprise a first channel corresponding towavelengths that are mostly below 580 nanometers and a second channelcorresponding to wavelengths mostly above 580 nanometers; the baselinepattern comprises: (i) first values, derived based on the first channelin the baseline images, which are indicative of the first and secondbaseline hemoglobin concentrations at the first and second regions,respectively, and (ii) second values, derived based on the secondchannel in the baseline images, which are indicative of third and fourthbaseline hemoglobin concentrations at the first and second regions,respectively; the current pattern comprises: (i) third values, derivedbased on the first channel in the current set of images, which areindicative of the first and second current hemoglobin concentrations atthe first and second regions, respectively, and (ii) fourth values,derived based on the second channel in the current set of images, whichare indicative of third and fourth current hemoglobin concentrations atthe first and second regions, respectively; whereby having separatevalues for different wavelengths enables to account for thermalinterference from the environment when detecting whether the user hasthe fever because thermal interference from the environment is expectedto affect the third values more than the fourth values.
 6. The system ofclaim 1, wherein the computer is further configured to calculate, basedon the current set of images, a current heart rate and/or a currentrespiration rate of the user, and to detect whether the user hashyperthermia or hypothermia based on the deviation of the currentpattern from the baseline pattern and deviations of the current heartrate and/or the current respiration rate from a baseline heart rateand/or baseline respiration rate of the user, respectively.
 7. Thesystem of claim 1, further comprising a short-wave infrared (SWIR)inward-facing head-mounted camera configured to detect wavelengths in atleast a portion of the range of 700 nm to 2500 nm; wherein the computeris further configured to utilize a deviation of a current SWIR patternfrom a baseline SWIR pattern taken while the user did not have a feverin the detection of whether the user has the fever.
 8. The system ofclaim 1, wherein the computer is further configured to detect blushingbased on the deviation of the current pattern from the baseline pattern,and present an alert to the user about the blushing.
 9. The system ofclaim 1, wherein the computer is further configured to utilize one ormore calibration measurements of the user's core body temperature, takenby a different device, prior to a certain time, to calculate the user'score body temperature based on a certain set of images that were takenby Cam_(1&2) after the certain time.
 10. The system of claim 1, whereinthe computer is further configured to calculate the user's core bodytemperature based on the deviation of the current pattern from thebaseline pattern.
 11. The system of claim 1, wherein the computer isfurther configured to calculate the values indicative of the baselineand current hemoglobin concentrations based on detecting imagingphotoplethysmogram signals in the baseline and current images.
 12. Amethod for detecting fever, comprising: receiving, from first and secondinward-facing head-mounted cameras (Cam_(1&2)) sensitive to wavelengthsbelow 1050 nanometer, images of respective first and second regions on auser's face; wherein middles of the first and second regions are atleast 4 cm apart; calculating, based on baseline images captured withCam_(1&2) while the user did not have a fever, a baseline patterncomprising values indicative of first and second baseline hemoglobinconcentrations at the first and second regions, respectively;calculating, based on a current set of images captured with Cam_(1&2), acurrent pattern comprising values indicative of first and second currenthemoglobin concentrations at the first and second regions, respectively;and detecting whether the user has a fever based on a deviation of thecurrent pattern from the baseline pattern.
 13. The method of claim 12,further comprising calculating, based on additional baseline imagescaptured with Cam_(1&2) while the user had a fever, a fever-baselinepattern comprising values indicative of first and second feverhemoglobin concentrations at the first and second regions, respectively;and detecting whether the user has the fever also based on a deviationof the current pattern from the fever-baseline pattern.
 14. The methodof claim 12, wherein the baseline images and the current set of imagescomprise a first channel corresponding to wavelengths that are mostlybelow 580 nanometers and a second channel corresponding to wavelengthsmostly above 580 nanometers; the baseline pattern comprises: (i) firstvalues, derived based on the first channel in the baseline images, whichare indicative of the first and second baseline hemoglobinconcentrations at the first and second regions, respectively, and (ii)second values, derived based on the second channel in the baselineimages, which are indicative of third and fourth baseline hemoglobinconcentrations at the first and second regions, respectively; thecurrent pattern comprises: (i) third values, derived based on the firstchannel in the current set of images, which are indicative of the firstand second current hemoglobin concentrations at the first and secondregions, respectively, and (ii) fourth values, derived based on thesecond channel in the current set of images, which are indicative ofthird and fourth current hemoglobin concentrations at the first andsecond regions, respectively.
 15. The method of claim 12, furthercomprising: calculating, based on the current set of images, a currentheart rate and/or a current respiration rate of the user, and detectingwhether the user has the fever, hyperthermia, or hypothermia also basedon deviations of the current heart rate and/or the current respirationrate from a baseline heart rate and/or baseline respiration rate of theuser, respectively.
 16. A non-transitory computer readable mediumstoring one or more computer programs configured to cause a processorbased system to execute steps comprising: receiving, from first andsecond inward-facing head-mounted cameras (Cam_(1&2)) sensitive towavelengths below 1050 nanometer, images of respective first and secondregions on a user's face; wherein middles of the first and secondregions are at least 4 cm apart; calculating, based on baseline imagescaptured with Cam_(1&2) while the user did not have a fever, a baselinepattern comprising values indicative of first and second baselinehemoglobin concentrations at the first and second regions, respectively;calculating, based on a current set of images captured with Cam_(1&2), acurrent pattern comprising values indicative of first and second currenthemoglobin concentrations at the first and second regions, respectively;and detecting whether the user has a fever based on a deviation of thecurrent pattern from the baseline pattern.
 17. The non-transitorycomputer readable medium of claim 16, further comprising instructionsfor execution of the following steps: calculating, based on additionalbaseline images captured with Cam_(1&2) while the user had a fever, afever-baseline pattern comprising values indicative of first and secondfever hemoglobin concentrations at the first and second regions,respectively; and detecting whether the user has the fever also based ona deviation of the current pattern from the fever-baseline pattern. 18.The non-transitory computer readable medium of claim 16, furthercomprising instructions for execution of the following steps:calculating, based on the current set of images, a current heart rateand/or a current respiration rate of the user, and detecting whether theuser has the fever, hyperthermia, or hypothermia also based ondeviations of the current heart rate and/or the current respiration ratefrom a baseline heart rate and/or baseline respiration rate of the user,respectively.
 19. The non-transitory computer readable medium of claim16, further comprising instructions for execution of a step involvingcalculating the user's core body temperature based on the deviation ofthe current pattern from the baseline pattern.
 20. The method of claim12, further comprising calculating the user's core body temperaturebased on the deviation of the current pattern from the baseline pattern.